DYNAMIC RESISTANCE BASED INTELLIGENT … RESISTANCE BASED INTELLIGENT RESISTANCE WELDING by MAHMOUD...

DYNAMIC RESISTANCE BASED INTELLIGENT RESISTANCE WELDING

by

MAHMOUD EL-BANNA

DISSERTATION

Submitted to the Graduate School

of Wayne State University,

Detroit, Michigan

in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY

2006

MAJOR: INDUSTRIAL ENGINEERING

Approved by:

______________________________ Advisor Date

______________________________

______________________________

______________________________

______________________________

© COPYRIGHT BY

MAHMOUD EL-BANNA

2006

All Rights Reserved

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DEDICATION

To my family; father, mother, brothers, sister and for the first baby in my family, my

nephew “Yanal”

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ACKNOWLEDGMENTS

I would like to acknowledge Advanced Manufacturing Technology Development

(AMTD) department at Ford Motor Company for supporting this research and providing

me with this unique opportunity to work in a real life project. In particular, I would like to

thank Dr. Dimitar Filev, Finn Tseng, Dave Chesney, Bill Moisson, Arnon Wexler,

Tamara Hanel and others for their recommendation and guidance throughout this work.

I would like also to acknowledge Welding Technology Corporation (WTC) for all

the support and the help they provided for this project. In particular, I would like to thank

John Vogeli, and Mike Clark, and others who facilitated and provided the required data

for this project.

Finally, I would like to thank Mrs. Lia Gyetvay from Roman Engineering, for the

help in editing the thesis.

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TABLE OF CONTENTS

Chapter Page

DEDICATION ...................................................................................................................ii

ACKNOWLEDGMENTS .................................................................................................. iii

LIST OF TABLES ........................................................................................................... vii

LIST OF FIGURES .......................................................................................................... x

CHAPTERS

CHAPTER 1 RESISTANCE SPOT WELDING ................................................................ 1

1.1 Resistance Spot Welding ..................................................................... 2

1.2 Types of Resistance Welding .............................................................. 5

1.3 Nondestructive testing techniques for Resistance Spot Welding ......... 6

1.3.1 Ultrasonic Technique ........................................................................ 6

1.3.2 Thermal Force Technique ............................................................... 11

1.3.3 Displacement Technique ................................................................ 12

1.3.4 Dynamic Resistance Technique ...................................................... 12

Interpretations of dynamic resistance curve .................................. 15

1.4 Statement of Proposed Research ...................................................... 19

1.5 Significance & Benefits ...................................................................... 22

CHAPTER 2 ONLINE QUALITATIVE NUGGET CLASSIFICATION BY USING LINEAR VECTOR

QUANTIZATION NEURAL NETWORK FOR RESISTANCE SPOT WELDING ........................ 24

2.1 Introduction ........................................................................................ 24

2.2 Constant heat control & Constant current control .............................. 30

Constant heat control (CHC) ......................................................... 31

v

Constant current control ................................................................ 33

2.3 Linear Vector Quantization (LVQ) network ........................................ 34

2.4 Experimental Setup ............................................................................ 36

2.5 Results ............................................................................................... 44

Constant Current Controller employing MFDC .............................. 44

Features Selection for MFDC Constant Current Control ............... 48

Alternating Current (AC) with constant heat control ...................... 50

2.6 Conclusions ....................................................................................... 54

CHAPTER 3 INTELLIGENT CONSTANT CURRENT CONTROL FOR RESISTANCE SPOT

WELDING ............................................................................................................. 56

3.1 Introduction ........................................................................................ 57

3.2 Intelligent Constant Current Control ................................................... 62

Soft Sensing of Expulsion Rate ..................................................... 63

Soft Sensing of Weld Quality ........................................................ 65

3.3 Fuzzy Logic Control Algorithm ........................................................... 67

3.4 Experimental Setup and Results ........................................................ 72

Intelligent Constant Current Control and Stepper Based Control

without Sealer ............................................................................... 75

Intelligent Constant Current Control and Stepper Based Control

with Sealer .................................................................................... 78

3.5 Conclusions ....................................................................................... 81

CHAPTER 4 ELECTRODE TIP DRESSING DETECTION BY USING FUZZY C–MEANS

CLUSTERING ALGORITHM IMPLEMENTED IN A HIERARCHAL FASHION ................... 83

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4.1 Introduction ........................................................................................ 83

4.2 Mechanisms of the Electrode Growth ................................................ 86

4.3 Electrode Tip Dressing ....................................................................... 87

4.4 Fuzzy C–Means Clustering Algorithm Implemented In a Hierarchal

Fashion .................................................................................................... 89

4.5 Experimental Setup ............................................................................ 91

4.6 Results ............................................................................................... 98

Constant heat control (CHC) ......................................................... 98

Constant Current Control (CCC) ................................................. 101

Principal Component Analysis (PCA) with Constant heat control

(CHC) .......................................................................................... 105

Principal Component Analysis (PCA) with Constant current control

(CCC) .......................................................................................... 109

4.7 Conclusions ..................................................................................... 112

CHAPTER 5 CONCLUSIONS AND FUTURE WORK ...................................................... 117

5.1 Conclusions ..................................................................................... 118

5.2 Recommendations for Future Work ................................................. 121

APPENDIX A EXPULSION DETECTION ALGORITHM .......................................................... 123

APPENDIX B FUZZY C-MEANS CLUSTERING ................................................................... 125

REFERENCES ............................................................................................................ 127

ABSTRACT ................................................................................................................. 136

AUTOBIOGRAPHICAL STATEMENT ......................................................................... 138

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LIST OF TABLES

TABLE PAGE

Table 1, Benefits and limitations of ultrasonic testing .................................................... 10

Table 2, Mechanical properties for the tested material .................................................. 42

Table 3, Element analysis for the base tested materials (weight percent) ..................... 42

Table 4, Element analysis of the coating substrate (weight percent) ............................ 43

Table 5, Coating weight ................................................................................................. 43

Table 6, Type (1) and Type (2) error ............................................................................. 45

Table 7, Type1 and 2 errors for classification of cold welds when using the entire

dynamic resistance profile with the neural network ................................................ 47

Table 8, Type 1 and 2 errors for normal welds classification when using the entire


Table 9, Type 1 and 2 errors for expulsion welds classification when using the entire


Table 10, Power of the test (1- β ) for different features for MFDC controller .............. 48

Table 11, Type1 and 2 errors for cold welds classification when using the maximum of


Table 12, Type1 and 2 errors for normal welds classification when using maximum of


Table 13, Type1 and 2 errors for expulsion welds classification when using maximum of


Table 14, Type1 and 2 errors for cold welds classification when using the entire

dynamic resistance profile with the neural network for AC controller ..................... 51

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Table 15, Type1 and 2 errors for normal welds classification when using the entire


Table 16, Type1 and 2 errors for expulsion welds classification when using the entire


Table 17, power of the test (1- β ) for different features for AC controller .................... 52

Table 18, Type1 and 2 errors for cold welds classification when using the features of the


Table 19, Type1 and 2 errors for normal welds classification when using the features of

the dynamic resistance profile with the neural network for AC controller ............... 53

Table 20, Type1 and 2 errors for expulsion welds classification when using the features

of the dynamic resistance profile with the neural network for AC controller ........... 54

Table 21, Mechanical properties for the tested material ................................................ 75

Table 22, Element analysis for the base tested materials (weight percent) ................... 75

Table 23, Number of expulsion welds for the fuzzy control scheme, and the

conventional stepper mode without sealer ............................................................. 78

Table 24, Number of cold welds for the fuzzy control scheme, the stepper, and the no

stepper modes without sealer ................................................................................ 78

Table 25, Number of expulsion welds for the fuzzy control scheme, the stepper, and the

no stepper modes with sealer ................................................................................ 81

Table 26, Number of cold welds for the fuzzy control scheme, the stepper, and the no

stepper modes with sealer ..................................................................................... 81

Table 27, Mechanical properties for the tested material ................................................ 96

Table 28, Element analysis for the base tested materials (weight percent) ................... 96

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Table 29, Element analysis of the coating substrate (weight percent) ......................... 97

Table 30, Coating weight ............................................................................................... 97

Table 31, Clusters obtained from the training welds for CHC ...................................... 100

Table 32, Size of the clusters obtained from the evaluation mode for CHC ................ 101

Table 33, Clusters obtained from the training welds for CCC ...................................... 102

Table 34, Size of the clusters obtained from the evaluation mode for CCC ................ 104

Table 35, Clusters obtained from the training welds for CHC when 4 principal

components were used as input for the algorithm ................................................ 107

Table 36, Size of the clusters obtained from the evaluation mode for CHC when 4

principal components were used as the input for the algorithm ........................... 108

Table 37, Clusters obtained from the training welds for CCC when 7 principal

components were used as input to the algorithm ................................................. 110

Table 38, Size of the clusters obtained from the evaluation mode for CCC when 7

principal components were used as the input for the algorithm ........................... 112

Table 39, Number and sizes of clusters obtained when using the entire cycle resistance

vector in case of CCC or the entire cycle voltage vector in case of CHC, as inputs

for the fuzzy C-mean clustering algorithm implemented in a hierarchal fashion .. 114

Table 40, Number and sizes of clusters obtained when using seven principal

components in case of CCC and four principal components in case of CHC, as

inputs for the fuzzy C-mean clustering algorithm implemented in a hierarchal

fashion ................................................................................................................. 116

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LIST OF FIGURES

FIGURE PAGE

Figure 1, Schematic diagram for resistance spot welding ............................................... 3

Figure 2, Schematic diagram for resistance spot welding Model .................................... 3

Figure 3, an illustration of a weld schedule ..................................................................... 4

Figure 4, transmitter and reflector sensor for ultrasonic technique .................................. 9

Figure 5, Ultrasonic transducer positioned far away from the electrode cap ................. 10

Figure 6, Ultrasonic transducer positioned near electrode cap ..................................... 10

Figure 7, Idealize force (right), Actual force (left) .......................................................... 12

Figure 8, Setup for spot welding with optical encoder sensor ....................................... 13

Figure 9, Resistance spot welding model for closed welding circuit .............................. 13

Figure 10, Schematic representation of resistance distribution on sheets ..................... 14

Figure 11, Schematic diagram of the conventional secondary dynamic resistance setup

............................................................................................................................... 15

Figure 12, , Dynamic resistance curve [37] ................................................................... 16

Figure 13, Different types of current applied in spot welding; AC, DC, CD, and MFDC

[51] ......................................................................................................................... 31

Figure 14, Thyristor circuit Symbol ................................................................................ 34

Figure 15, LVQ architecture .......................................................................................... 35

Figure 16, AC Schematic Welder .................................................................................. 38

Figure 17, MFDC Schematics Welder [58] .................................................................... 39

Figure 18, MFDC Constant current control Profile ........................................................ 39

Figure 19, AC Constant heat control Profile .................................................................. 40

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Figure 20, Schematic for set up test .............................................................................. 41

Figure 21, Sequence and dimensions of coupon used in experiment ........................... 41

Figure 22, Small and Large coupons sequence in each batch ...................................... 43

Figure 23, Dynamic resistance for cold, expulsion and normal welds for MFDC with

constant current control .......................................................................................... 47

Figure 24, Dynamic resistance for cold, expulsion and normal welds for AC with

constant heat control .............................................................................................. 50

Figure 25, Fuzzy Control Scheme after the first weld .................................................... 63

Figure 26, Secondary resistance profiles for cold, expulsion and normal welds for MFDC

constant current control .......................................................................................... 64

Figure 27, LVQ network model [7]. P is the input vector of size N, W1, and S1, 2 are the

weight matrices and the number of neurons in the first and second layer. ............. 67

Figure 28, Membership functions for 'E' the number of expulsion welds and “N” the

number of normal welds in the last 'p' welds .......................................................... 71

Figure 29, Schematics of MFDC Welder [53] ................................................................ 73


Figure 31, Secondary current using the fuzzy model .................................................... 76

Figure 32, Secondary current for the stepper model without sealer .............................. 77

Figure 33, spot secondary current for the fuzzy control scheme with sealer ................. 79

Figure 34, spot secondary current for the stepper mode with sealer ............................. 80

Figure 35, Fuzzy C-means clustering Algorithm implemented in a hierarchal fashion for

tip dressing quality detection .................................................................................. 91

Figure 36, Tip dressing hierarchy fuzzy clustering ........................................................ 92

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Figure 37, Alternating Current controller (AC) Schematic diagram ............................... 93


Figure 39, Secondary current profile for constant heat control (CHC) ........................... 95

Figure 40, Secondary current profile for constant current control (CCC) ...................... 95

Figure 41, Coupon used in CHC and CCC test ............................................................. 97

Figure 42, counter shows number of welds on each batch for CHC test ....................... 98

Figure 43, Number of clusters obtained from training mode (6 clusters) for CHC. ........ 99

Figure 44, Clustering of weld data for CHC test .......................................................... 100

Figure 45, counter shows number of welds on each batch for CCC test ..................... 102

Figure 46, Number of clusters obtained from training mode (4 clusters) for CCC. ...... 103

Figure 47, Number of clusters obtained from validation mode (4 clusters) for CCC .... 104

Figure 48, Scree plot for training CHC welding data ................................................... 106

Figure 49, Number and size of clusters obtained from training mode (5 clusters) for

CHC when 4 principal components were used as input to the algorithm ............. 106

Figure 50, Clustering of weld data for CHC test when 4 principal components were used

as input for the algorithm ...................................................................................... 108

Figure 51, Scree plot for training CCC welding data ................................................... 109

Figure 52, Number and size of clusters obtained from training mode (7 clusters) for

CCC when 7 principal components were used as input to the algorithm ............. 110

Figure 53, Clustering of weld data for CCC test when 7 principal components were used

as input for the algorithm ...................................................................................... 111

Figure 54, Dynamic resistance for cold, expulsion and good welds for MFDC constant

current control ...................................................................................................... 124

1

CHAPTER 1

RESISTANCE SPOT WELDING

For several decades, resistance spot welding has been an important process in

sheet metal fabrication. The automotive industry, for example, prefers spot welding for

its simple and cheap operation. The advantages of spot welding are many and include

the following: an economical process, adaptable to a wide variety of materials (including

low carbon steel, coated steels, stainless steel, aluminum, nickel, titanium, and copper

alloys) and thicknesses, a process with short cycle times, and a relatively robust

process with some tolerance to fit-up variations.

However, given the uncertainty associated with individual weld quality (attributed

to factors such as tip wear, sheet metal surface debris, fluctuations in power supply

etc.), it is a common practice in industry to add a significant number of redundant welds

to gain confidence in the structural integrity of the welded assembly. In recent years,

global competition for improved productivity and reduced non-value added activity, is

forcing companies such as the automotive OEMs to eliminate these redundant spot

welds. In order to minimize the number of spot welds and still satisfy essential factors

such as strength and surface integrity, weld quality must be obtained.

Traditionally, to check weld quality, destructive and nondestructive tests are used

on randomly sampled work pieces at the production site. These processes tend to be

predominantly off-line or end-of-line processes. While this information is of good value,

there is often too much delay to utilize the information to control the process on-line.

Weld quality estimation must be done in real time to monitor and repair weld defects as

they occur, and more importantly, to control the process (for example, through controller

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set point adjustment).

Destructive testing of car sub-assemblies and/or complete car bodies in three of

the United States' biggest car manufacturing plants costs an estimated $400 to $600

million per year through loss of value-added inventory. If 50% of the components,

including spot welds, could be tested non-destructively and then sold, the potential cost

saving would be approximately $200 to $300 million per year. Reducing by 10% the

number of redundant welds in a car may yield savings of $400 to $600 million/year.[1]

This chapter is organized as follows: Resistance welding model, Types of

resistance welding, Nondestructive testing techniques for resistance spot welding,

Statement of proposed research, and Significance and benefits.

1.1 Resistance Spot Welding

Figure (1), shows a schematic diagram for resistance spot welding. It consists

mainly of primary (High voltage, low current) and secondary circuits (low voltage, high

current). The resistance welding process employs a combination of pressure and heat

to produce a weld between the work pieces in the secondary circuit. Resistance heating

occurs as electrical welding current flows through the work pieces. The work pieces are

generally in the secondary circuit of a transformer. The transformer converts high-

voltage, low current commercial power into suitable high current, low voltage welding

power.

The heat generated by current flow, Figure (2), may be expressed as follows:

E=I2×R× t

3

Sheet Metal

Transformer

Primary Circuit Secondary Circuit

Sheet Metal

Transformer

Sheet MetalSheet Metal

Transformer

Primary Circuit Secondary Circuit

Figure 1, Schematic diagram for resistance spot welding

Figure 2, Schematic diagram for resistance spot welding model

where

E = Heat generated (joules)

4

I = Current (amperes)

R =Resistance (ohms)

t = Duration of current flow (seconds)

The quantity of energy required to produce a given resistance weld is determined

by several factors. Key factors are the weld area (heated volume), the peak

temperature, the specific heat of the work pieces, and the heat loss through the

surrounding metal and electrodes. An increase in magnitude of one or more of these

factors requires a corresponding increase in energy to produce the weld.

A typical spot welding operation is controlled by a weld schedule, whose time

steps are controlled by a spot welding controller. A weld schedule is usually divided into

four steps, as shown in Figure (3):

Figure 3, An illustration of a weld schedule

• Squeeze time, or the time between the first application of electrode force and the

first application of welding current.

• Weld time, or the actual time the current flows.

5

• Hold time, or the period during which electrode force is applied and the welding

current is shut off.

• Off time, or the period during which the electrodes are not contacting the work

pieces.

1.2 Types of Resistance Welding

There are four different types of resistance welding [2]:

1. Spot Welding: A resistance welding process wherein coalescence is produced by the

heat obtained from the resistance to the flow of electric current through the work

parts held together under pressure by electrodes. (The size and the shape of the

individually formed welds are limited primarily by the size and contour of the

electrodes).

2. Roll Spot Welding: The making of separated spot welds with (rotating) circular

electrodes.

3. Seam Welding: A resistance welding process wherein coalescence is produced by

the heat obtained from resistance to the flow of electric current through the work

parts held together under pressure by circular electrodes. The resultant weld is a

series of overlapping spot welds made progressively along a joint by rotating the

electrodes.

4. Projection Welding: A resistance welding process wherein heat produces

coalescence obtained from resistance to the flow of electric current through the work

parts held together under pressure by electrodes. The resultant welds are localized

at predetermined points by the design of the parts to be welded. The localization is

usually accomplished by the projections, embossments or intersections.

6

1.3 Nondestructive testing techniques for Resistance Spot Welding

Literature offers several different nondestructive evaluation techniques for weld

quality evaluation. The most promising techniques can be roughly grouped into four

major groups: Ultrasonic and Acoustic Emission techniques, Thermal Force techniques,

Displacement techniques, and finally Dynamic Resistance techniques. It should be

noted here that some of these techniques are more compatible than others for on-line

evaluation and control. In addition, some of these techniques tend to be very intrusive

and/or expensive for wide-scale deployment (for example, the ultrasonic technique),

and in that sense, not compatible today for mainstream resistance welding. The rest of

this section briefly describes these nondestructive evaluation techniques.

1.3.1 Ultrasonic Technique

Ultrasonic is a technique that measures the response to an artificial and

repeatable acoustic excitation of the object under evaluation [3]. Sound that is above

the range of human hearing (20 KHz) is referred to as ultrasound. For most common

contact material inspection applications, the frequencies used are 1.0, 2.25 and 5.0

MHz. The high frequencies of ultrasound do not travel through air as well as through

liquids and solids. There are two methods in ultrasonic testing:

1. Transmission technique: means that one sensor is the sender and the other sensor

is the receiver. (Figure 4)

2. Reflection technique: means that the sensor itself is the sender and the receiver.

(Figure 4)

7

Figure 4, Transmitter and reflector sensor for ultrasonic technique

Figure 5, Ultrasonic transducer positioned far away from the electrode cap

Ultrasonic technique has two major flaws; the price is still expensive comparable

to other testing techniques and the size of ultrasonic sensor is still large. More benefits

and limitations of ultrasonic testing are summarized in Table (1).

8

Table 1, Benefits and limitations of ultrasonic testing

Benefits Limitations High sensitivity to both surface and

subsurface discontinuities

Surface must be accessible to transmit

ultrasound

Superior depth of penetration for flaw

detection or measurement

Skill and training is more extensive than with

some other methods.

Single-sided access is adequate

when pulse-echo technique is used

Normally requires a coupling medium to

promote better transfer of sound energy into

specimen

Provides high accuracy in

determining reflector position and

estimating size and shape

Materials that are rough, irregular in shape, very

small, exceptionally thin or not homogeneous

are difficult to evaluate

Minimal part preparation required

Cast iron and other coarse grained materials are

difficult to inspect due to low sound transmission

and high signal noise

Real-time evaluation is feasible

(hardware and software readily

available)

Linear defects oriented parallel to the sound

beam may go undetected

Detailed images can be produced

with automated systems

Reference standards are required for both

equipment calibration and characterization of

flaws

Can facilitate other measurements

such as thickness

One of the major problems until now in applying the ultrasonic technique is to

choose the position of the Ultrasonic sensor; near the electrode tip or away from the tip

[4]. When positioning far from the caps, Figure (5) the ultrasonic transducer will normally

be fixed at the electrode holder. The transducer will be installed in a way that the

9

ultrasonic will be induced directly into the cooling water pipe, which is situated in the

electrode shaft.

Figure 6, Ultrasonic transducer positioned near electrode cap

The coupling is accomplished by means of the water, which is flowing through

the cooling water pipe. However, the position has the following disadvantages:

• Influence to the measuring accuracy by changes of conditions between

transducer and cap, for example:

o Loosing or missing cooling water pipe

o Too short cooling water pipe

o Deviation of cooling water Temperature

o Gas bubbles in cooling water

10

• Can’t be applied when the electrode holder is missing or when tongs arms that

has large inclination or are curved.

On the other hand, the other option is that the sensor is positioned at the end of the

electrode shaft directly near the cap, Figure (6), in order to induce the sound directly

into the electrode cap. The coupling will not be realized by the cooling water, but by a

coupling pad, which is fixed at the transducer. The possible displacement of the cap at

the cone will be caught up by the transducer housing which is spring-suspended.

Advantages of positioning the ultrasonic sensor near the electrode caps are:

• Little measurement scattering, thereby higher rate classification of spot weld

quality.

• No influence of measuring results at a change of state of cooling water.

• Integration of ultrasonic transducer at curved tongs arms and missing electrode

holders possible.

• Service and maintenance friendly implementation.

Ultrasonic technique has been explored by many researchers [5-15]. In order to

estimate the spot weld quality, the most important thing that should be known is the

three dimensional geometry of the weld nugget. The consistent result from literature is

that the geometry can be determined by measuring the transit time and the attenuation

of the ultrasonic wave (or echo) propagated through the weld nugget in a direction

perpendicular to the faces of the sheet metal stack. On the contrary, Kannatey Asibu

[16] shows that the analysis of the raw acoustic emission (AE) signals as well as the

spectrum and RMS values revealed no specific correlation between the AE signals and

nugget formation.

11

While the ultrasonic methods have shown good promise in laboratory

environments, from a practical of point view, acoustic sensors cannot be easily mounted

and maintained on weld guns (calibration will be necessary at regular intervals). They

also tend to affect negatively the circulation of the coolant within the tips, besides the

wiring problems that will limit the movement of the robot when holding the welding gun,

and the susceptibility of the signal to magnetic field fluctuations.

1.3.2 Thermal Force Technique

Thermal expansion caused by a growing weld nugget will be felt by the welding

gun as “Thermal Forces”. This will indicate to the controller whether sufficient weld

nugget growth has been achieved. The thermal force feedback system exploits the fact

that thermal forces precisely reflect the state of the metal during the welding process.

There is a distinct difference between the applied welding force, which is an

important parameter of a resistance welding process for it ensures electrical contact and

reduces the odds of weld nugget expulsion, and thermal force.

In the welding process, the force reaches a preset value in the squeeze stage,

theoretically keeping constant during the weld stage, holds for a short period after the

current terminates, and then releases. In reality, however, the force varies during the

weld stage; the weld stage is the most important among the four stages. Figure (7)

Thermal force technique has also been widely explored by many researchers

[17-26]. From a practical point of view, the fundamental drawback with this system is

that the weld gun has to be structurally very rigid (heavy) so as to be able to accurately

transfer (and measure) the very small displacements to the load cell. In addition, the

added weight tends to increase maintenance problems with the robots.

12

Figure 7, Idealize force (right), Actual force (left)

1.3.3 Displacement Technique

The displacement technique directly measures nugget formation and expansion

displacement between the electrodes, Figure (8), and have also been widely explored

[27-36]. A number of control systems have been based on this principle. A linear

variable differential transformer (LVDT) is typically used to measure electrode

displacement. In order to avoid the noise from the magnetic field, in some cases, the

displacement is measured with a digital optical encoder. The fundamental limitation with

this technique is the lack of robustness and accuracy in estimating the weld quality.

From a practical point of view, wiring and magnetic field problems exist.

1.3.4 Dynamic Resistance Technique

In a RSW process, a machine forms a closed circuit with the secondary circuit of

a transformer, mechanical assembly, and sheet metal to be welded. The closed circuit

can be modeled in terms of their individual resistances, as shown in Figure (9). In this

resistance model, the electrical resistances of the transformer, the mechanical

13

assembly, and the sheet metal are represented as Rt, Rm, and Rl, respectively. The

resistances Rt and Rm can be reasonably assumed constant during the process.

The sheet metal resistance (Rl) consists of three components:

1. The bulk resistance of the sheet metal (Rb)

2. The interface resistance between electrodes and sheet metal (Rc)

3. The contact resistance on the faying surface (Rf).

.

Figure 8, Setup for spot welding with optical encoder sensor

Figure 9, Resistance spot welding model for closed welding circuit

14

If two pieces of sheet metal of equal thickness are welded, as shown in Figure (10),

then

Rl = 2Rb + 2Rc + Rf

Figure 10, Schematic representation of resistance distribution on sheets

Figure 11, Schematic diagram of the conventional secondary dynamic resistance setup

15

The measurements of voltage and current (at primary or secondary side) are

used to calculate dynamic resistance, Figure (11). The word dynamic comes from the

resistance that changes during the welding time (each half cycle). Measurement of

dynamic resistance has been one of the most effective techniques for quality monitoring

and estimation during the past several decades. Some of the earliest and simplest

techniques involved monitoring the voltage and current in the secondary circuit. These

electrical parameters, however, fluctuate heavily during welding cycles.

Interpretations of dynamic resistance curve

Dickinson et al [37] proposed five stages to characterize the dynamic resistance

during welding of steels based on the competition between bulk resistance and contact

resistance, Figure (12).

In stage I, the sheet metal is brought into contact under the pressure provided by

the electrode force. This creates areas of electrical contact at the points where

asperities on the surfaces meet. Voltage is applied between electrodes causing current

to flow at the micro contact points. The resistance between electrodes at this point is

equal to the bulk resistance of the two sheet metals, the two electrodes to sheet contact

resistance, and faying resistance between sheet metals.

Under normal conditions, surface films, oxide layers, or other contaminants will

be present on the work pieces. Since these are essentially insulators, the initial contact

resistance will be very high. Therefore, the initial generation of heat will be concentrated

at the surfaces, especially at the faying interface between sheets. This heat will cause

the surface contaminants to break down, resulting in a very sharp drop resistance.

16

Figure 12, , Dynamic resistance curve [37]

In Stage II, immediately after the break down of surface contaminants, a metal to

metal contact exists. However, the surface resistance may still remain relatively high

due to limited area for current flow provided by the asperities contacts. Heating then is

concentrated at the faying interface region, and the temperature in this region and in the

bulk material will increase. As heating progresses, the asperities soften and the contact

area increases, thus causing the resistance to decrease. At the same time an

increasing in temperature, results in increasing resistivity, thus providing an opposite

effect. The competition between these two mechanisms determines whether resistance

is increasing or decreasing and thus determines the position of the α minimum.

Eventually, the increase in contact area will be overcome by the increasing temperature

effect, and the total resistance will begin to rise.

In Stage III, the increase in resistivity resulting from increasing temperature

dominates the resistance curve in this region. The end of stage III should correspond to

17

local melting beginning to occur at the asperities contacts. The transition to stage IV will

probably occur near the inflection point in the curve ( 2

2

dtRd =0).

In Stage IV, there are three mechanisms influence this stage. The bulk of the

work pieces continue to increase in temperature, thus causing resistivity and resistance

to increase. But, the heat being generated also causes additional melting to occur at the

surfaces, increasing the size of the molten region and the cross sectional area available

for the current flow. This mechanism causes a resistance to decrease. Also, the

increased softening will result in some mechanical collapse, shortening the path for the

current flow and decreasing resistance. The β peak is a consequence of the

temperature beginning to stabilize, while nugget growth and mechanical collapse begin

to dominate, and therefore resistance starts to decrease.

In Stage V, beyond the β peak, the growth of the molten nugget and mechanical

collapse continue to cause resistance to decrease. If the nugget grows to a size such

that it can no longer be contained by the surrounding solid metal under the compressive

electrode force, expulsion will occur.

In early studies, Roberts [38] observed the changes in resistance of the welds in

resistance spot welding, according to various material combinations. Owing to the lack

of suitable instrumentation at that time, it was hard to present an effective resistance

measurement system and to understand the physical meaning of the variations in

resistance during the welding process. Later, Savage et al [39] used an oscilloscope to

measure the welding voltage (detected directly from the welding specimen using clips)

and the welding current (measured in the shunt of the secondary circuit). The dynamic

resistance could then be estimated using the recorded graph. . In this research, the

18

dynamic resistance is calculated using the current and voltage at the peak current point

(i.e., 0di dt = ), in order to effectively eliminate inductive noise.

Research continued and more accurate and efficient methods have been

developed to estimate dynamic resistance. Dickinson [37] applied the root mean square

(RMS) value of the monitored signal using an analog circuit. Thornton et al [40] studied

the contact resistance of aluminum alloy and dynamic resistance changes according to

ASTM specifications [3]. Kaiser et al [41] used the dynamic resistance, which was

calculated by dividing the peak voltage by the corresponding peak current, to observe

the changes in dynamic resistance according to the current. When the dynamic

resistance pattern and weld lobe curve were considered together, the beta peak of the

dynamic resistance was observed earlier as the weld current and surface resistance

increased.

With the development of measuring devices and hardware, many methods for

measuring dynamic parameters have been considered, Gedeon [32]. A system of

measuring the dynamic resistance using a microprocessor was proposed by Patange

[42]. In that study, the weld’s current, which was measured in the current transformer

(CT) of the secondary circuit, was used to measure the instantaneous dynamic

resistance when the current derivative reaches zero. The microprocessor inspected the

weld quality on the basis of this value.

Given its definite physical meaning and ease of measurement, many studies on

the secondary dynamic resistance have been performed. Through these studies, the

relationship between the pattern of secondary dynamic resistance and the nugget

growth has been determined for uncoated steel (see for example, Savage [39] and

19

Dickinson [37]). While the dynamic resistance is very promising for online spot-weld

quality estimation, it has many limitations. The fundamental issues have to do with the

location of the voltage measuring device and the increased cost of installing the

monitoring device.

Cho and Rhee [43] show that the process variables, which were monitored in the

primary circuit of the welding machine, are used to obtain the variation of the dynamic

resistance across electrodes. This allows the dynamic resistance monitoring system to

be applied to the in-process system without any extra monitoring devices in the

secondary circuit. Also, in order to test the reliability of such a system, an artificial

intelligence algorithm was developed to estimate the weld quality using the primary

dynamic resistance.

Lee et al [44] propose a quality assurance technique for resistance spot welding

using a neuro-fuzzy algorithm. Four parameters from an electrode separation signal, in

the case of non-expulsion, and dynamic resistance patterns, in the case of expulsion,

are selected as the fuzzy input parameters. These parameters are determined using a

neuro-learning algorithm and then are used to construct a fuzzy inference system.

Wang and Wei [45] showed that dynamic resistance can also be obtained by

taking the sum of temperature-dependent bulk resistance of the work pieces and

contact resistances (at the faying surface and electrode-work piece interface) within an

effective area corresponding to the electrode tip where welding current primarily flows.

1.4 Statement of Proposed Research

Resistance spot welding (RSW) is one of the most critical processes employed

for sheet metal assembly, in particular, by the automotive industry. Although used in

20

mass production for several decades, RSW poses several major problems, most

notably, large variation in weld quality. The strategy employed by the automobile OEMs

to reduce the risk of part failure is to often require more welds to be performed than

would be needed to maintain structural integrity if each weld was made reliably [46].

Advances over the last decade in the area of non-intrusive electronic sensors, signal

processing algorithms, and computational intelligence, coupled with drastic reductions in

computing and networking hardware costs, have now made it possible to develop non-

intrusive intelligent resistance welding systems that overcome the above shortcomings.

The research develops an Intelligent Resistance Welding (IRW) System that

improves the weld quality and reduces the number of welds needed. In particular, there

are three specific research achievements:

• Development of an algorithm for accurate in-process non-destructive

evaluation (NDE) of nugget quality by using the dynamic resistance (or

secondary voltage) profile during the welding process for coated steels.

The problem of real time estimation of the weld quality from process data is one

of the key objectives in the present weld control systems. This task can be alleviated if

the weld controller is equipped with a voltage sensor in the secondary circuit. Further

simplification that significantly increases the feasibility of the mission of indirect

estimation of weld quality follows from replacing the goal of quantifying the weld quality

in terms of button size by the more modest objective of indirect estimation the class of

the weld, e.g. satisfactory (acceptable, “normal” button size) unsatisfactory (under sized,

“cold” welds), and defects (“expulsion”). Nugget quality classification was employed by

21

using computational intelligence methods (in particular, by using Linear Vector

Quantization (LVQ) neural network).

• Development of a closed-loop supervisory control scheme for adapting

with time the controller set points for weld quality enhancement.

The closed-loop procedures will employ temporal information made available by

the NDE algorithm (i.e. nugget quality classification by using linear vector quantization

(LVQ)) regarding the most recent welds to adjust online the welding process parameters

(i.e. weld current level). Thus, the system can partially account for process variation

attributed to factors such as electrode tip-wear, electrode misalignments, and material

non-uniformity. To achieve this goal an adaptive fuzzy control scheme is developed and

verified based on detecting the expulsion and the normal welds in the last recent welds.

By keeping the weld status just below the expulsion level, optimum weld strength is

achieved.

• Development of an algorithm for on line evaluation of the electrode health

condition subsequent to electrode tip dressing operation.

Coated sheet metal has been widely used recently in the automotive industry and

others to improve the corrosion resistance in auto body constructions. However, one of

the major concerns of using the coated sheet metal is that the electrode life can be

significantly shorter than the bare (uncoated) sheet metal. To elongate the electrode life

significantly when using coated sheet metal, electrode tip dressing should be performed

frequently. Until now, there is no model to check the electrode tip dressing quality;

therefore, a hierarchal fuzzy C-mean clustering algorithm is developed and verified for

22

on line detecting the electrode health condition after the electrode tip dressing is

performed.

1.5 Significance & Benefits

The benefits from developing the proposed Intelligent Resistance Welding

system for sheet metal assembly in the automotive industry can be roughly grouped into

two categories:

• Lowered Production and Testing Costs: As stated earlier, the prevalent

strategy currently employed by the automobile industry is to reduce the risk of

part failure (given a lack of confidence in the quality of individual welds) is to

often require more welds than would be actually needed. If the weld quality

enhancements offered by the proposed IRW system can result in a 10%

reduction in the number of welds required in a car, the savings can amount to

$400 to $600 million/year.[1] Additional savings would come from reduced

dependence on standard destructive weld quality tests (such as the chisel and

hammer method accompanied by visual inspection), and destructive testing of

car sub-assemblies and/or complete car bodies. Reducing by 10% these sorts of

tests should yield savings of $40 to $60 million per year.[1]

• Improved Driver Safety and Customer Satisfaction: In delivering high-quality

welds, the proposed intelligent resistance welding system will enhance the

structural integrity of the overall vehicle, and in turn, improves driver safety while

minimizing life-time vehicle maintenance cost.

23

Similar benefits can be expected in other industries that heavily utilize resistance

spot welding.

The dissertation is organized as follows:

Chapter two presents an algorithm for nugget quality classification by using

Linear Vector Quantization (LVQ) neural network for both types of controller; Medium

Frequency Direct Current (MFDC) and Alternating Current (AC).

Chapter three presents an intelligent constant current control algorithm based on

fuzzy logic scheme for Medium Frequency Direct Current (MFDC).

Chapter four presents an algorithm for on line evaluation of electrode health

condition subsequent to electrode tip dressing cycle for Constant Current Control (CCC)

and Constant Heat Control (CHC) in Alternating Current (AC).

Chapter five gives the conclusions of this research and the recommendations for

future work.

24

CHAPTER 2

ONLINE QUALITATIVE NUGGET CLASSIFICATION BY USING LINEAR VECTOR

QUANTIZATION NEURAL NETWORK FOR RESISTANCE SPOT WELDING

Real-time estimation of weld quality from process data is one of the key

objectives in present weld control systems for resistance spot-welding process. This

task can be alleviated if the weld controller is equipped with a voltage sensor in the

secondary circuit. Replacing the goal of quantifying the weld quality in terms of button

size by the more modest objective of indirect estimation the class of the weld, e.g.

satisfactory (acceptable, “normal” button size) unsatisfactory (under sized, “cold” welds),

and defects (“expulsion”), further improves the feasibility of the mission of indirect

estimation of weld quality. This paper proposes an algorithmic framework based on

Linear Vector Quantization (LVQ) neural network for estimation of button size based on

a small number of dynamic resistance patterns for cold, normal, and expulsion welds

that are collected during the stabilization process.

Nugget quality classification by using LVQ network was tested on two types of

controllers; Medium Frequency Direct Current (MFDC) with constant current controller,

and Alternating Current (AC) with Constant Heat controller.

In order to reduce the dimensionality of the input data vector, different sets of

features are extracted from the dynamic resistance profile and compared by using

power of the test criteria. Results from all these investigations are very promising and

reported here in detail.

2.1 Introduction

25





low carbon steel, coated steels, stainless steel, aluminum, nickel, titanium, and copper

alloys) and thicknesses, a process with short cycle times, and a relatively robust

process with some tolerance to fit-up variations. However, given the uncertainty

associated with individual weld quality (attributed to factors such as tip wear, sheet

metal surface debris, fluctuations in power supply etc.), it is a common practice in

industry to add a significant number of redundant welds to gain confidence in the

structural integrity of the welded assembly. In recent years, global competition for

improved productivity and reduced non-value added activity is forcing companies such

as the automotive OEMs to eliminate these redundant spot welds. In order to minimize

the number of spot welds and still satisfy essential properties such as strength, weld

quality must be obtained.

Traditionally, to check weld quality, destructive tests (the dominant method of

inspection in industry) and nondestructive tests are used on randomly sampled work

pieces at the production site. These processes also tend to be predominantly off-line or

end-of-line processes. While this information is of good value, there is often too much

delay in collection the information to utilize it for controlling the process. Weld quality

estimation must be done in real-time to monitor and repair weld defects as they occur,

and more importantly, to control the process.

26

Literature offers several different nondestructive evaluation techniques for weld

quality evaluation. The most promising techniques can be roughly grouped into four

major groups: Ultrasonic technique, Thermal Force technique, Displacement technique,

and finally, Dynamic Resistance technique. It should be noted here that some of these

techniques are more compatible than others for on-line evaluation and control. In

addition, some of these techniques tend to be very intrusive and/or expensive for wide-

scale deployment (for example, the ultrasonic technique), and in that sense, not

compatible today for main-stream resistance welding. The rest of this section briefly

describes these nondestructive evaluation techniques.

Ultrasonic technique has been explored by many researchers [5, 7, 9-12, 14, 15,

47-49]. In order to estimate the spot weld quality, the most important thing that should

be known is the three dimensional geometry of the weld nugget. The consistent result

from literature is that the geometry can be determined by measuring the transit time and

the attenuation of the ultrasonic wave (or echo) propagated through the weld nugget in

a direction perpendicular to the faces of the sheet metal stack. On the contrary,

Kannatey-Asibu shows that the analysis of the raw acoustic emission (AE) signals as

well as the spectrum and RMS values revealed no specific correlation between the AE

signals and nugget formation. While the ultrasonic methods have shown good promise

in laboratory environments, from a practical point view, acoustic sensors cannot be

easily mounted and maintained on weld guns (calibration will be necessary at regular

intervals). They also tend to negatively affect the circulation of the coolant within the

tips, besides the wiring problems that will limit the movement of robot holding the

welding gun, and the susceptibility of the signal to magnetic field fluctuations.

27

Thermal force technique has also been widely explored by many researchers

[17, 18, 20-25, 32, 50]. There is a distinct difference between the applied welding force,

which is an important parameter of a resistance welding process for it ensures electrical

contact and reduces the odds of weld nugget expulsion, and thermal force. Thermal

expansion caused by the growing weld nugget will be felt by the welding gun as

“Thermal Forces”. This will indicate to the controller whether sufficient weld nugget

growth has been achieved. The thermal force feedback system exploits this fact that

thermal forces precisely reflect the state of the metal during the welding process. From

a practical point view, the fundamental drawback with this system is that the weld gun

has to be structurally very rigid (heavy) so as to be able to accurately transfer (and

measure) the very small displacements to the load cell. Besides, the added weight

tends to increase maintenance problems with the robots. It has only limited success on

certain type of weld guns (C type).

The displacement technique directly measures nugget formation and expansion

displacement between the electrodes and have also been widely explored [27-30, 32,

33, 35, 36, 51-53]. A number of control systems have been based on this principle. A

linear variable differential transformer (LVDT) is typically used to measure electrode

displacement. In order to avoid the noise from the magnetic field, in some cases, the

displacement is measured with a digital optical encoder. The fundamental limitation with

this technique is the lack of robustness. From a practical point view, the wiring and

magnetic field problems will also be there.

Given its definite physical meaning and ease of measurement, many studies on

the secondary dynamic resistance have been performed. Through these studies, the

28

relationship between the pattern of secondary dynamic resistance and the nugget

growth has been determined (see for example, Savage and Dickinson ). Cho and Rhee

[43] show that the process variables, which were monitored in the primary circuit of the

welding machine, are used to obtain the variation of the dynamic resistance across

electrodes. This allows the dynamic resistance monitoring system to be applied to the

in-process system without any extra monitoring devices in the secondary circuit. In

addition, to test the reliability of such a system, an artificial intelligence algorithm was

developed to estimate the weld quality using the primary dynamic resistance. Cho and

Rhee used uncoated steel welding (low carbon cold rolled steel) to verify their model.

However, coated steel (i.e. hot dip galvanized steel) is the material mainly used in the

auto industry and others to reduce corrosion. They also used shear strength as weld

quality metric, while the auto industry and others use the button diameter as their weld

quality metric. Lastly, their tests were performed on Alternating Current (AC) controller,

while Medium Frequency Direct Current (MFDC) is still not examined yet.

Lee et al [44] propose a quality assurance technique for resistance spot welding

using a neuro-fuzzy algorithm. Four parameters from an electrode separation signal, in

the case of non-expulsion, and dynamic resistance patterns, in the case of expulsion,

are selected as the fuzzy input parameters. These parameters are determined using a

neuro-learning algorithm and then are used to construct a fuzzy inference system. They

also used the displacement and the voltage signals as inputs to their model. Using the

displacement signal is not very practical in industry. They also used shear strength as

weld quality metric. Again, the test was performed on AC controller, while MFDC is not

examined. Podrzaj et al [24] proposed a linear vector quantization (LVQ) neural network

29

system to detect expulsion. The network is analyzed with different sensor combinations

and different materials. The results show that the LVQ neural network is able to detect

the expulsion in different materials. The experiment also points to the welding force

signal as the most important indicator of the expulsion occurrence. They used voltage

and other sensors for expulsion detection, while cold and normal welds detection was

not explored. While they identify welding force signal as the most important indicator for

the expulsion occurrence, availability of force signal is limited to certain types of guns,

and they are more expensive than other types of sensors. Once again, the test was

performed on AC controller, while MFDC is not examined.

Park and Cho [54] used LVQ as well as a multi-layer perceptron (MLP) neural

network to classify the weld quality (strength and indentation) by using the force signal.

They classify the weld quality into five different categories: (I) insufficient welding state,

(P) poor welding state, (G) good welding state, (R) rich welding state, and (E) excess

weld state. The results show that the LVQ and MLP neural networks have a success

rate of 90 % and 95% for the test data, respectively. They also used force signal as

input, shear strength as weld quality metric, and only tested the model using mild steel.

Tests were again performed on AC controller and MFDC is not examined.

This chapter deals with an algorithm for classification of button quality based on a

small number of patterns for cold, normal, and expulsion welds that are collected during

the stabilization process. Linear vector quantization (LVQ) network will be used to

predict the three different categories for nugget quality (expulsion, normal, and cold

welds) from dynamic resistance profile. LVQ shows good performance for complex

classification problems because of its fast learning nature, reliability, and convenience

30

of use. It particularly performs well with small training sets. This property is significantly

important for industrial application, where training data is very limited; take considerable

time, cost, or even impractical to get more data.

The rest of this chapter is organized as follows: Section 2 outlines the basic

principles behind constant heat and constant current controllers; Section 3 briefly

describes linear vector quantization (LVQ) neural network; Section 4 and 5 describe the

experimental setup and the results obtained, respectively; Section 6 finally offers some

concluding remarks.

2.2 Constant heat control & Constant current control

Resistance spot welding machines are usually based on Constant Current

Control (CCC) or Constant Voltage Contol (CVC). A constant current control machine

will vary its output voltage to maintain a steady current while a constant voltage control

machine will fluctuate its output current to maintain a set voltage. Recently, Hasegawa

[55] introduced a new type of controller based on specific heat.

Figure (13) illustrates typical welding current types and profiles applied in

resistance welding including the single phase alternating current (AC) (most prevalent

controller in industry), the three phase direct current (DC), the condensator discharge

(CD), and the newly developed medium frequency inverter DC (MFDC). Usually, the

root mean square (RMS) values of the welding current are used in the machine

parameter settings and the process controls. It is often tedious for welding engineers to

optimize the welding current profile and amplitude for any given welding application.

31

The proposed LVQ classification network will be tested on the two most popular

types of controllers; Constant Heat Controller (CHC) and Constant Current Controller

(CCC). A brief description of these controllers follows.

Figure 13, Different types of current applied in spot welding; AC, DC, CD, and MFDC [51]

Constant heat control (CHC)

Constant Heat Controller is a new type of controller based on a specific heat

concept (i.e. amount of heat induced per unit volume). In order to demonstrate CHC

principles, let us introduce some notation. Let "I" denote the secondary current per half

cycle, "R" the secondary resistance per half cycle, and thc the half cycle time. The total

heat per half cycle (J) is then:

2hcJ I R t= × × (1)

32

If "A" denotes the current cross area, "L" the sheet metal stack thickness, "Jm"

the heat induced per unit volume per half cycle, then the total heat per half cycle (J) is:

mJ J A L= × × (2)

If we assume that resistivity " ρ " is constant during the half cycle, then the

resistance per half cycle "R" is:

LRA

ρ ×= (3)

Knowing that thc =1 2F , where "F" is the frequency (60Hz), and using ohms law

(V=I×R), where V is the secondary voltage per half cycle, equation (1) can be arranged

as follows:

FRVJ

××=

2

2

(4)

Using equations (2), (3) and (4), we can obtain the following expression for the

specific heat per half cycle:

2

2

2 LFVJ m

×××=

ρ (5)

The target specific heat per weld Jv is then:

∑=

=N

iimv JJ

1, (6)

where N is the total number of half cycles per weld.

Per equation (5), given that ρ , F, and L are all assumed to be constant and

known, to calculate the specific heat per half cycle Jm, the only measurement necessary

is that of the secondary voltage V.

33

The target specific heat per unit volume per weld Jv can be also calculated from:

⎟⎠⎞

⎜⎝⎛ ×

+=L

timeWeldlevelWeldJv 56.20.9 (7)

where “Weldtime” denotes number of cycles usually taken from standard tables

and “Weldlevel” denotes a trial and error value to be determined during stabilization

process to produce good normal welds.

Constant Heat Controller (CHC) tries to match the total sum of target heat energy

per half cycle (equation 6) with a predetermined specific heat per weld Jv (equation 7),

by adjusting the primary current in each half cycle.

Constant current control

Constant Current Control (CCC) is the most common type of controller used in

industry for its simplicity, reliability, and performance.

In order to understand its operational principles clearly, we first need to

understand the thyristor principle. Thyristor is a solid-state semiconductor device that is

similar to a diode but with an extra terminal used to turn it on, as illustrated in Figure

(14). Once turned on, the thyristor will remain on (conducting) as long as there is

significant current flowing through it. If the current falls to zero, the device switches off.

Thyristors are mainly used when high currents and voltages are involved, and

are often used to control alternating currents where the change of sign of the current

causes the device to automatically switch off. This principle is used to control the

desired loading by adjusting the frequency of the sinusoidal input. The range of

frequencies is large as there is no limit to the number of cycles a thyristor can perform

(exhibits no "wear out" modes). With phase angle control, a thyristor can be turned on at

34

a specific and adjustable portion of the cycle of the controlling sinusoidal input. Moving

the point at which the thyristor is turned on regulates power output [56].

Figure 14, Thyristor circuit Symbol

In CCC, a predetermined current is used as set point, and this current is

converted to target phase angle by using a standard table. Once the target phase angle

is determined, the secondary current (or the primary current multiplied by the

transformer ratio) is measured and compared with the set point, and the phase will be

adjusted depending on the difference between the target current (set point) and the

measured (output) current.

2.3 Linear Vector Quantization (LVQ) network

Learning vector quantization (LVQ) is a method for training competitive layers of

a neural network in a “supervised” manner. As illustrated in Figure (15), it consists

mainly of three layers; input layer, competitive layer, and output layer. The “classes” that

the competitive layer finds are dependent only on the distance between input vectors. If

two input vectors are very similar, the competitive layer assigns them to the same class.

35

LVQ shows good performance for complex classification problems because of its fast

learning nature, reliability, and convenience of use. It particularly performs well with

small training sets. This property is significantly important for industrial application,

where training data is very limited; take considerable time, cost, or even impractical to

get more data.

Figure 15, LVQ architecture

The network parameters are as follows: P denotes the input vector, N the size of

the input vector, Wi the weight matrix for the ith layer, Si number of neurons in the ith

layer, ni the net input vector of the ith layer, and ai the output of the ith layer.

The first layer (competitive layer) is used to find the prototype vector W1s (i.e., a

row of the weight matrix W1) that points in the direction closest to the input vector, i.e.,

Mini 21

i−P W i∀ , where i∈ (1, 2…S1)

The neurons that possess the least distance between vector weight matrix and

input vector are assigned a value of one and the other neurons are assigned a value of

zero.

36

Finally, the output layer (linear layer) joins the subclasses (S1) from the

competitive layer and W2 weight matrix into target classes (S2) through a linear transfer

function.

Matrix W2 remains constant where as W1 changes during the training process.

The weights of the winning neuron (a row of the input weight matrix) are adjusted using

the Kohonen learning rule. For example, supposing that the ith neuron wins the

competition, the elements of the ith row of the input weight matrix are adjusted as shown

below:

w1(i) = w1(i-1) + α (P(i)- w1(i-1)),

where P(i) is the input vector of the ith iteration and α is the learning rate.

If just the Kohonen learning rule is employed, the neural network is called LVQ1.

LVQ2 is an improved version of LVQ1, with the main difference being that in the latter

case, the prototype vectors of two neurons are updated if the input vector P(i) is

classified incorrectly. The weights of the neuron that wrongly won the competition are

also updated as follows:

w1(i) = w1(i-1) - α (P(i) - w1(i-1))

2.4 Experimental Setup

In general, there are two types of resistance welders: alternating current (AC)

type illustrated in Figure (16), and direct current (DC) type. A DC resistance welding

controller provides the advantage that the current supplied to the weld can be controlled

within stringent limits. However, there are two major disadvantages: the equipment

required is expensive and the electrodes wear out quickly because current flows in one

37

direction only during welding. In contrast, an AC resistance welding controller provides

the advantages that the equipment required is inexpensive and the electrodes wear out

very slowly. However, a disadvantage is that the current supplied to the weld can be

controlled only within fairly loose time interval [57].

To overcome the disadvantages of DC, Medium Frequency Direct Current

(MFDC) type of welding controller, illustrated in Figure (17), is also employed for this

investigation. In particular, MFDC Constant Current controller (CCC) is employed in

which the controller tries to achieve a constant set point (constant current) in each

millisecond within the weld, Figure (18), but the current can be changed from weld to

weld.

On the other hand, in Constant Heat Control (CHC), specific heat (total power

per unit volume) is used to adjust the welding current to an optimum value to

consistently achieve sturdy welds. The specific heat required to satisfactorily weld the

workpieces is calculated from total thickness of the workpieces and welding time. From

this calculated specific heat, specific heat per unit time is calculated. The CHC adjusts

welding current, Figure (19), to an optimum value required to produce the required

specific heat per unit time.

The experimental setup for MDFC and AC controller is shown in Figure (20).

MFDC welding machine capacity is 180 KVA with 680 lb welding force provided by a

servo gun. HWPAL25 electrode type with 6.4 mm face diameter is used. Welding time

used is 233 milliseconds with 11.5 KA as initial input secondary current with an

incremental stepper of 1 ampere per weld. On the other hand, AC welding machine

capacity is 180 KVA with 680 lb welding force provided by a pneumatic gun. HWPAL25

38

truncated electrode type with 6.4 mm face diameter is used with a welding time of 16

cycles and 11.3 KA as initial input secondary current.

Two metal stacks are used for both MFDC and CHC tests; 2.00 mm gage hot tip

galvanized HSLA steel with 0.85 mm gage electrogalvanized HSLA steel. Tables 2 and

3 show the mechanical properties and element analysis for the tested materials, while

tables 4 and 5 show the element analysis and coating weight for coatings substrate.

Small coupons (1"×12") with 6 welds in each (first weld as anchor weld) and large

coupons (4"×12") with 72 welds in each (first weld in each column as anchor weld) are

used for testing, as illustrated in Figure (21).

Figure 16, AC Schematic Welder

39

Figure 17, MFDC Schematics Welder [58]

0 50 100 150 200 2502000

3000

4000

5000

6000

7000

8000

9000

10000

11000

12000

WeldingTime(milisecond)

Seco

ndar

y C

urre

nt (A

)

Figure 18, MFDC Constant current control Profile

40

Figure 19, AC Constant heat control Profile

Eleven batches of 300 welds each (total 3300 welds without anchor welds

counted), were performed with 10 tips dressed after each batch. In the case of MFDC

test, for each batch, Figure (22), 10 small coupons with 5 welds each (total 50 welds

each batch without anchor weld counted) were peeled and the maximum and minimum

nugget diameters were measured. Thus, the nugget diameter is measured for a total of

550 welds; 411 were found to be good welds, 22 were cold welds, and 117 welds with

expulsion.

In the case of CHC test, 120 small coupons were pealed, and the quality of the

welds was checked visually. The total number of investigated welds was 720; 509 were

found to be normal welds, there were no cold welds, and 211 welds were observed with

expulsion.

41

Figure 20, Schematic for set up test

Figure 21, Sequence and dimensions of coupon used in experiment

42

Table 2, Mechanical properties for the tested material

Material Type

0.85 mm gage, HSLA, electrogalvanized

2.00 mm gage, HSLA, hot dip galvanized

0.2% Yield (MPa) 234 406

Tensile (MPa) 333 474

% Elongation 2 in.(51 mm) gage

38 31

Table 3, Element analysis for the base tested materials (weight percent)

Element 0.85 mm gage, HSLA, electrogalvanized


Carbon 0.01 0.09 Manganese 0.20 0.46 Phosphorous 0.02 0.01 Sulfur 0.01 0.01 Silicon <0.03 0.03 Copper 0.01 0.08 Nickel 0.02 0.03 Chromium 0.03 0.07 Vanadium <0.01 0.02 Molybdenum <0.01 0.01 Aluminum 0.05 0.02 Titanium <0.01 <0.01 Tin <0.01 <0.01 Iron Base Base

43

Figure 22, Small and Large coupons sequence in each batch

Table 4, Element analysis of the coating substrate (weight percent)



Aluminum 0.005 1.0 Nickel 0.065 <0.001 Zinc Balance Balance

Table 5, Coating weight

Material Coating Weight

(g/m2)

0.85 mm gage, HSLA, electrogalvanized 0.70/0.64

2.00 mm gage, HSLA, hot dip galvanized 0.85/1.35

44

2.5 Results

As stated earlier, the objective here is to develop an on-line nugget quality

classification algorithm that employs a Linear Vector Quantization (LVQ) neural network

and to investigate its efficacy on a constant current controller that employs Medium

Frequency Direct Current (MFDC) and a constant heat controller that employs

Alternating Current (AC). The results will be reported in terms of type 1 error (α ) and

type 2 error ( β ) for cold, normal, and expulsion welds. As per the definitions in Table 6,

Type 1 error (α ) (known as false alarm rate) defines the probability of rejecting the null

hypothesis, while it is true. For example, if the null hypothesis defined the weld as

expulsion weld, Type 1 error (α ) defines the probability that the weld is misclassified

as normal or cold weld, while it really is an expulsion weld. Type 2 error ( β ) (known as

failed alarm) defines the probability of not to reject the null hypothesis, while it is false.

It is important to note that that there is a trade off between type (1) error and type

(2) error. If the model is too sensitive (i.e., type (2) error is very low) it is normal to have

a larger number of false alarms (i.e., type (1) error will be high).

Constant Current Controller employing MFDC

As mentioned before, eleven batches of 300 welds each (total 3300 welds

without anchor welds counted), were performed with 10 tips dressed after each batch.

For each batch, 10 small coupons- with 5 welds each (total 55 welds each batch without

anchor weld counted)-were peeled. The total number of investigated welds is 550; 411

were found to be normal welds, 22 cold welds, and 117 welds with expulsion.

45

Table 6, Type (1) and Type (2) error

Statistical

Decision

True State of Null Hypothesis

Ho is true Ho is false

Reject Ho Type (1)

Errorα Correct

Don’t reject Ho Correct Type (2)

Error β

In all tests, the classification of nugget quality is based on resistance profile.

Figure (23), shows an illustrative dynamic resistance profile for three types of welds;

cold, normal, and expulsion, for MFDC with constant current controller. It can be seen

that these profiles are not easily distinguishable. The cold weld dynamic resistance

profile tends to be lower than the other profiles, while the expulsion weld dynamic

resistance profile tends to have a sharp drop especially towards the end.

In this test, LVQ2 network was trained on three, six, and five patterns for cold,

normal, and expulsion welds, respectively. Twelve hidden neurons were used with a

learning rate of 0.01.

Tables 7, 8, and 9 report type 1 errors (α ) and type 2 errors ( β ) for cold, normal,

and expulsion welds when using the entire dynamic resistance profile as an input vector

to the LVQ neural network. It can be seen that the percent of false alarms are lowest for

the cold weld case at 10%, 26% for normal welds, and 44% for expulsion welds. As for

type 2 errors, they are once again lowest for cold welds at 0%, 16% for expulsion welds,

and 44% for normal welds.

46

In order to reduce the dimensionality of the input resistance vector to the LVQ

neural network, different features are entertained in place of the whole vector, and

include:

• Maximum value of the input resistance vector

• Minimum value of the input resistance vector

• Mean value of the input resistance vector

• Standard deviation value of the input resistance vector

• Range value of the input resistance vector

• Root mean square (RMS) value of the input resistance vector

• First region slope (S1) value of the input resistance vector

• Second region slope (S2) value of the input resistance vector

0 50 100 150 200 250100

120

140

160

180

200

220

240

Welding Time(millisecond)

Dyn

amic

Res

ista

nce

(mic

ro o

hm)

Cold Weld

Normal Weld

Expulsion Weld

47

Figure 23, Dynamic resistance for cold, expulsion and normal welds for MFDC with constant current control

• Third region slope (S3) value of the input resistance vector

• Fourth region slope (S4) value of the input resistance vector

• Binned RMS vector of input resistance: Input resistance is divided into 5 bins and

RMS values are calculated for each bin

Table 7, Type1 and 2 errors for classification of cold welds when using the entire dynamic resistance profile with the neural network

Ho: Weld is Cold True State of Null Hypothesis

Statistical

Decision Ho is true Ho is false

Reject Ho α =0.00 1-α =1.00

Don’t reject Ho 1- β =0.90 β =0.10

Table 8, Type 1 and 2 errors for normal welds classification when using the entire dynamic resistance profile with the neural network

Ho: Weld is

Normal True State of Null Hypothesis

Statistical


Reject Ho α =0.45 1-α =0.55


Table 9, Type 1 and 2 errors for expulsion welds classification when using the entire dynamic resistance profile with the neural network

Ho: Weld is Expulsion True State of Null Hypothesis

48

Statistical Decision Ho is true Ho is false

Reject Ho α =0.63 1-α =0.37


Features Selection for MFDC Constant Current Control

The criteria for features selection was based on power of the test (i.e. 1- β ) for

the cold, normal, and expulsion welds as shown in Table 10 . The feature that gives the

highest classification percentages for the three types of welds will be chosen as input

for LVQ network. In order to simplify features selection, we assume that interactions

among features are neglected.

In our work, we just employed the most promising feature identified by power of

the test criteria, maximum value of the input resistance vector, as input for LVQ neural

network. Tables 11, 12, and 13 report the type 1 and 2 error results from the network

when just employing this feature. It can be seen that both types of errors are reduced by

using the maximum resistance feature instead of the entire vector of resistance for

normal and expulsion welds. On the other hand, for cold welds, the type 2 error

degrades.

Table 10, Power of the test (1- β ) for different features for MFDC controller

Feature Cold Welds Normal Welds Expulsion WeldsMaximum 99.8% 78.6% 83.0% Minimum 94.6% 13.0% 100.0% Mean 98.3% 13.7% 100.0% Standard deviation 74.9% 60.3% 72.2% Range 100.0% 38.2% 75.0% Root Mean Square (RMS) 92.1% 14.5% 100.0% Slope 1 53.6% 80.2% 79.2% Slope 2 67.7% 100.0% 30.7%

49

Slope 3 73.9% 90.1% 45.8% Slope 4 100.0% 37.4% 99.8% Bin 1 83.6% 31.3% 76.2% Bin 2 90.7% 16.0% 88.7% Bin 3 89.6% 14.5% 100.0% Bin 4 92.1% 100.0% 14.4% Bin 5 98.1% 20.6% 98.6%

Table 11, Type1 and 2 errors for cold welds classification when using the maximum of dynamic resistance profile with the neural network


Statistical


Reject Ho α =0.00 1-α =1.00


Table 12, Type1 and 2 errors for normal welds classification when using maximum of dynamic resistance profile with the neural network

Ho: Weld is


Statistical


Reject Ho α =0.29 1-α =0.71


Table 13, Type1 and 2 errors for expulsion welds classification when using maximum of dynamic resistance profile with the neural network



Reject Ho α =0.23 1-α =0.77


50

Alternating Current (AC) with constant heat control

In this case, as mentioned before, 120 small coupons were pealed and the

quality of the welds was checked visually. The total number of investigated welds was

720; 509 were found to be normal welds, there were no cold welds, and 211 welds were

observed with expulsion.

In all tests, the classification of nugget quality is based on the dynamic resistance

profile. Figure (24), shows an illustrative dynamic resistance profile for the three types of

welds; cold, normal, and expulsion, for AC constant heat controller. It can be seen that

these profiles are not easily distinguishable. Usually, the cold weld dynamic resistance

profile tends to be lower than the other profiles, while the expulsion weld dynamic

resistance profile tends to have a sharp drop especially towards the end.

In this test, LVQ network was trained on two, six, and five patterns for cold,

normal, and expulsion welds, respectively. Twelve hidden neurons were used with a

learning rate of 0.01

0 5 10 15 20 25 3070

75

80

85

90

95

100

105

110

115

Time in half cycle

Dyn

amic

Res

ista

nce

(mic

ro o

hm)

Normal Welds

Expulsion Welds

Figure 24, Dynamic resistance for cold, expulsion and normal welds for AC with constant heat control

51

Tables 14, 15, and 16 report type 1 and type 2 errors for cold, normal, and

expulsion welds when using the entire dynamic resistance profile as an input vector to

the LVQ neural network with AC controller. Given that no cold welds were observed

during experimentation, false alarms are not applicable ‘NA’. False alarm rate for normal

welds is lowest at 5% in comparison with expulsion welds at 37%.

As for type 2 errors, the failed alarm rates were lowest for expulsion welds at 1%,

3% for cold welds, and 36% for normal welds.

Table 14, Type1 and 2 errors for cold welds classification when using the entire dynamic resistance profile with the neural network for AC controller


Statistical


Reject Ho α =NA 1-α =NA


Table 15, Type1 and 2 errors for normal welds classification when using the entire dynamic resistance profile with the neural network for AC controller

Ho: Weld is


Statistical


Reject Ho α =0.05 1-α =0.95


52

Table 16, Type1 and 2 errors for expulsion welds classification when using the entire dynamic resistance profile with the neural network for AC controller



Reject Ho α =0.37 1-α =0.63


In order to once again reduce the dimensionality of the input resistance vector to

the LVQ neural network, different features are entertained in place of the whole vector

(same initial features used in MFDC constant current controller with five RMS bins).

Features screening was once again performed using the power of the test

criteria, with ignoring interactions between features. The minimum feature was used as

input for LVQ neural network as shown in Table 17.

Table 17, power of the test (1- β ) for different features for AC controller

Feature Cold Welds Normal Welds Expulsion WeldsMaximum 86.8% 97.7% 16.6% Minimum 98.3% 76.1% 95.7% Mean 96.8% 50.3% 77.5% Standard deviation 100.0% 95.4% 41.7% Range 90.4% 82.2% 88.4% Root Mean Square (RMS) 96.7% 57.9% 75.9% Slope 1 100.0% 83.4% 24.0% Slope 2 71.8% 57.5% 76.5% Slope 3 98.6% 0.0% 100.0% Slope 4 100.0% 27.9% 96.8% Bin 1 94.3% 19.6% 93.7% Bin 2 96.7% 1.5% 100.0% Bin 3 98.5% 0.3% 100.0% Bin 4 96.7% 23.7% 93.0% Bin 5 100.0% 83.0% 69.1%

53

Tables 18, 19, and 20 shows type 1 and type 2 errors for cold, normal, and

expulsion welds when using the “minimum” feature of the dynamic resistance vector as

an input to the neural network. It can be noticed that type (1) errors are reduced by

using the “minimum” feature instead of the entire dynamic resistance vector for normal

and expulsion welds.

On the other hand, type (2) errors are reduced for cold and normal welds, while it

increased for the expulsion welds, when using the “minimum” feature instead of the

entire dynamic resistance vector.

The false alarm rate for normal welds at 5% is lower than rate for expulsion

welds at 37%. The failed alarm rate for expulsion welds is the lowest at 1%, and the

rates are 3% and 36% for expulsion and normal welds.

Table 18, Type1 and 2 errors for cold welds classification when using the features of the dynamic resistance profile with the neural network for AC controller


Statistical


Reject Ho α =NA 1-α =NA


Table 19, Type1 and 2 errors for normal welds classification when using the features of the dynamic resistance profile with the neural network for AC controller

Ho: Weld is


Statistical


54

Reject Ho α =0.04 1-α =0.96


Table 20, Type1 and 2 errors for expulsion welds classification when using the features of the dynamic resistance profile with the neural network for AC controller



Reject Ho α =0.25 1-α =0.75


2.6 Conclusions

The problem of real time estimation of the weld quality from the process data is

one of the major issues in the weld quality process improvement. This is particularly the

case for resistance spot welding. Most of the models offered in the literature to predict

nugget diameter from the process data employ measurements such as voltage and

force and are not suitable in an industrial environment for two major reasons: the input

signals for prediction model are taken from intrusive sensors (which will affect the

performance or capability of the welding cell), and, the methods often required very

large training and testing datasets.

In order to overcome these short comings, we propose a Linear Vector

Quantization (LVQ) neural network for nugget quality classification that employs the

easily accessible dynamic resistance profile as input. The goal is to make an on-line

distinction between normal welds, cold welds, and expulsion welds. Our additional goal

is to address this task when employing two types of weld controllers: Constant Current

Controller that employs Medium Frequency Direct Current and a Constant Heat

55

Controller that employs Alternating Current. The results from applying the LVQ neural

network trained using very limited data collected during the stabilization process are

very promising and are reported in detail. In addition, we report very promising results

when a reduced feature set is employed for classification rather than the complete

dynamic resistance profile. The features were selected using power of test criteria.

Overall, the results are very promising for developing practical on-line quality

monitoring systems for resistance spot-welding machines.

56

CHAPTER 3

INTELLIGENT CONSTANT CURRENT CONTROL FOR RESISTANCE SPOT

WELDING

Resistance spot welding is one of the primary means of joining sheet metal in the

automotive industry and other industries. The demand for improved corrosion resistance

has led the automotive industry to increasingly use zinc coated steel in auto body

construction. One of the major concerns associated with welding coated steel is the

mushrooming effect (the increase in the electrode diameter due to deposition of copper

into the spot surface) resulting in reduced current density and undersized welds (cold

welds). The most common approach to this problem is based on the use of simple

unconditional incremental algorithms (steppers) for preprogrammed current scheduling.

In this paper, an intelligent algorithm is proposed for adjusting the amount of current to

compensate for the electrodes degradation. The algorithm works as a fuzzy logic

controller using a set of engineering rules with fuzzy predicates that dynamically adapt

the secondary current to the state of the weld process. The state is identified by

indirectly estimating two of the main process characteristics - weld quality and expulsion

rate. A soft sensor for indirect estimation of the weld quality employing an LVQ type

classifier is designed to provide a real time approximate assessment of the weld nugget

diameter. Another soft sensing algorithm is applied to predict the impact of changes in

current on the expulsion rate of the weld process. By maintaining the expulsion rate just

below a minimal acceptable level, robust process control performance and satisfactory

weld quality are achieved. The Intelligent Constant Current Control for Resistance Spot

Welding is implemented and validated on a Medium Frequency Direct Current (MFDC)

57

Constant Current Weld Controller. Results demonstrate a substantial improvement of

weld quality and reduction of process variability due to the proposed new control

algorithm.

3.1 Introduction

The demand to improve corrosion resistance has led the auto industry to use

coated steel, which has resulted in stringent requirements on conventional weld

controllers that employ “stepper” type preprogrammed current scheduling. The main

objective of the weld current stepper is to maintain weld nugget size within acceptable

limits while at the same time minimizing electrode growth. Large current steps could

lead to an increase in electrode tip growth due to the use of high current levels. This in

turn requires even larger increases in current, thereby causing a runaway process of

electrode growth. Under these conditions, weld size would deteriorate at a rapid rate.

On the other hand, small increases in welding current result in a slow rate of electrode

tip growth, which is advantageous in terms of electrode life, provided the small

increases in current are sufficient to maintain adequate current density to produce the

required weld nugget size.

A basis for setting up a current stepper can be developed by determining the

pattern of electrode growth obtained in a particular welding cell. Typically, test

procedures suitable for this purpose include the standard electrode life test and the

dynamic/oscillating weldability lobe. Different approaches are used for setting up a weld

current stepper, including subjective methods, fixed increments, constant current

density, gradient following, and iterative approaches.

58

In a subjective or "best guess" approach, current steps are based on maintaining

a slight red glow at the electrode/sheet interface and/or regularly adjusting the current to

a value immediately below the splash or expulsion level. This approach has been found

to give significant improvements in electrode life. While acceptable results can be

achieved by this means, an extreme skill is required in determining the point at which

current is to be increased.

In a fixed (preprogrammed scheduling) increment approach, a current stepper

can be based on increasing either the heat control (i.e. phase shift control) or the actual

welding current, in fixed increments after performing a predetermined number of welds.

Generally, the increment of phase shift can be set between 1% and 5%. It was

concluded [59] that a stepper function based on a fixed increment of the heat control or

phase shift control was not a viable means of extending electrode life in many

instances.

In a constant current density approach, a stepper based on maintaining a

constant current density (current per electrode diameter) that also keeps the electrode

force constant, has been investigated by Williams [59]. It was observed that this

approach was unacceptable due to high rates of electrode growth that occurred.

In a gradient following approach, the gradient of the dynamic weldability lobe can

give a good indication of the optimum stepper. To construct the dynamic weldability

lobe, the welding current is set to achieve a weld diameter equivalent to 5 t (where “t”

is the smallest thickness between the sheet metal to be welded) and welds are

produced until the weld size falls to say 3.5 t . At that point, the current is increased

to return the weld size to 5 t and welding continued at this current level until the weld

59

size again falls to 3.5 t . The current is again increased to give 5 t weld size and

the process repeated to maintain the weld size between 5 t and 3.5 t .An

indication of current stepper requirement, in terms of the number and level of steps, can

be derived from the average slope of the dynamic weldability lobe. The problem with

this approach is the sensitivity of electrode life to the magnitude of the current steps

used to accommodate electrode growth. It is a general experience that small increases

in current at frequent intervals are more beneficial than large infrequent steps. However,

the use of smaller than ideal current steps near the start of an electrode campaign may

result in a reduction in weld size to an unacceptable level. In addition, the gradient of

the dynamic weldability lobe is influenced by coating type.

The iterative approach, developed by Williams and Holiday [60], involves

recalculating the weldability lobe limits by taking into account the higher rate of

electrode growth. The first stage involves calculation of the current I and the area A

necessary to obtain the current density I/A at the electrode contact face at the start of

the welding process. This current density would cause electrode tip growth at a certain

rate dA/dn, where ‘n’ is number of welds, which in turn would necessitate a certain rate

of current increase dI/dn. Based on this current increase and the length of the step,

defined in terms of the number of welds, a new current level is then calculated.

Similarly, a new electrode tip contact area A is calculated from the rate of increase in

the contact area dA/dn. This completes the iteration. A new current density is then

recalculated from these values, with subsequent values for the rate of tip growth dA/dn

and rate of current increases dI/dn calculated each iteration. The main disadvantage of

this approach is that it results in too rapid growth in the electrode diameter.

60

An alternative fuzzy control approach was developed by Messler [61] based on

electrode displacement signal to adjust power delivered to the welds in real time. The

fuzzy control scheme applied to resistance spot welding was capable of adjusting every

weld whose actual electrode displacement curve deviated from the desired or the ideal

electrode displacement curve that produces a good weld. The control actions involve:

(1) Increasing the level of applied current or % heat input anytime the actual profile falls

below the desired one, but in accordance with tuned rules to avoid under or

overshooting, and (2) Reducing or withholding current flow or heat input anytime the

actual curve rises above the desired curve in accordance with tuned rules. The main

problem with this approach is that the signal obtained from intrusive sensor (electrode

displacement) makes the applicability of this approach very difficult, if not impossible, for

industrial implementation.

Chen and Araki [62] proposed a fuzzy control algorithm to adjust the current level

during the production of the weld, by estimating different stages in the weld process

using the dynamic resistance profile. The dynamic resistance profile is divided into four

different stages; S1: transitional period, S2: nugget forming staring period, S3: nugget

size enlarging period, and S4: heat holding period. In S2 and S3, a larger welding

current may be applied to make nugget forming quick, and in S1 and S4 a smaller

welding current can be applied to reduce energy loss and electrodes degradation in the

welding process. This approach adjusts the welding current within welds and not

between welds, which is limited by the capability of the control to adjust the current in a

very short time. It doesn't take into account how the current can be adjusted when

expulsion or cold welds occur.

61

Lee et al. [63] proposed a quality assurance technique for resistance spot

welding using a neuro-fuzzy algorithm. Four features from an electrode separation

signal (in the case of non expulsion) and dynamic resistance features (in the case of

expulsion) are selected as fuzzy inputs. The error in the predicted strength was within

± 4%. Again, the assumption of using intrusive sensor (electrode displacement) limits

the applicability of this approach.

In this chapter, we propose a novel intelligent control algorithm that addresses

the problem of constant current weld control of coated steel in the presence of

significant electrode degradation. The algorithm operates as a fuzzy logic controller

using a set of engineering rules with fuzzy predicates that dynamically adapt the

secondary current to the state of the weld process. Since the direct measurement of the

main process characteristics - weld quality and expulsion rate - is not feasible in an

industrial environment these variables are estimated by soft (indirect) sensors.

A soft sensor for indirect estimation of the weld quality employing a Learning

Vector Quantization (LVQ) type classifier is designed to provide a real time approximate

assessment of the weld nugget diameter.

Another soft sensing algorithm that is based on continuous monitoring of the

secondary resistance is applied to predict the impact of the current changes on the

expulsion rate of the weld process.

The main objective of the rule set of the fuzzy logic control algorithm is to

describe a nonlinear control strategy that adjusts the secondary current to maintain the

expulsion rate just below a minimal acceptable level guaranteeing satisfactory weld

quality and robust process control performance. The fuzziness of the rules predicates

62

reflects the uncertainty of the indirectly estimated weld quality and expulsion rate

variables. The Intelligent Constant Current Control for Resistance Spot Welding was

implemented and validated on a Medium Frequency Direct Current (MFDC) Constant

Current Weld Controller. Results demonstrate a substantial improvement of weld quality

and reduction of process variability due to the proposed new control algorithm.

The next sections are organized as follows. Section II describes the overall

intelligent control algorithm and the soft sensors for estimation of weld quality and

expulsion rate. Section III presents the fuzzy logic control algorithm. Emphasis is given

on the engineering considerations behind the control rules and the implementation of

these rules to tune the secondary current. Section IV reviews the experimental

conditions and results. Conclusions are presented in the last section.

3.2 Intelligent Constant Current Control

In this section we present an intelligent control algorithm that replaces the

conventional “stepper” type constant current weld control scheme. The current remains

unchanged during the weld but the primary current level is continuously adjusted based

on the estimated state of the weld process during the last p welds (parameter p

represents the size of a moving window). The main process characteristics – the

expulsion rate and the size of the weld nugget – are not directly measurable but are

derived from the secondary resistance profiles of the last p welds. The secondary

resistance is calculated from the measured secondary voltage and the calculated

secondary current (Figure 25).

63

Soft Sensing of Expulsion Rate

Expulsion refers to the ejection of molten metal from the weld fusion zone during

the welding process. This is an undesirable phenomenon due to detrimental effect on

weld nugget integrity (the loss of metal from the fusion zone can reduce the weld size

and result in weld porosity), which may significantly reduce the strength and durability of

the welded joints [64]. Some of the main factors that have an impact on expulsion are

insufficient electrode force, excessive heating, worn electrodes, and poor sheet surface

condition.

Ip

Measure Secondary Current

Measure Secondary Voltage

Calculate Secondary Resistance

Fire Primary Current

LVQ based Quality Nugget

Estimation

Expulsion Detection

Fuzzy Control Algorithm

IsVs

Rs

N

E

dα

Z-1

*Iin: Input Current (Start)Ip: Primary Current Is: Secondary CurrentVs: Secondary VoltageRs: Secondary Resistance

N: Number of normal welds from LVQE: Number of expulsion welds from expulsion algorithmIold: Old primary currentdα: Change of current gain

Iold

Welding Process

Intelligent Constant Current Control

*Input Current (first weld only)

Iin

Ip

Measure Secondary Current

Measure Secondary Voltage

Calculate Secondary Resistance

Fire Primary Current

LVQ based Quality Nugget

Estimation

Expulsion Detection

Fuzzy Control Algorithm

IsVs

Rs

N

E

dα

Z-1

*Iin: Input Current (Start)Ip: Primary Current Is: Secondary CurrentVs: Secondary VoltageRs: Secondary Resistance

N: Number of normal welds from LVQE: Number of expulsion welds from expulsion algorithmIold: Old primary currentdα: Change of current gain

Iold

Welding Process

Intelligent Constant Current Control

*Input Current (first weld only)

Iin

Figure 25, Fuzzy Control Scheme after the first weld

64

On the other hand, in order to get the optimum strength for the weld, the input

parameters (current, time, force) need to be targeted just below the expulsion level

[24].

Expulsion is estimated indirectly from the resistance profile. The main indicator

for expulsion, as pointed out in [24, 37, 64], is the instantaneous drop in the resistance

(Figure 26). In this chapter we use a modified version of the expulsion algorithm from

reference [55].

0 50 100 150 200 25060

80

100

120

140

160

180

Dyn

amic

Res

ista

nce

(mic

ro o

hm)

Welding Time (milli seconds)

Normal Weld

Cold Weld

Expulsion Weld

Figure 26, Secondary resistance profiles for cold, expulsion and normal welds for MFDC constant current control

Lets R(k) denote the secondary resistance value at the current millisecond cycle

(the MFDC weld process takes 233 mS), and R(k-1) and R(k-2) the previous resistance

values.

65

The soft sensing expulsion algorithm continuously checks for a resistance drop

(after the cooling period, in our experiment after 67 milliseconds) that is represented by

the following condition for the resistance:

If Max{R(k-2),R(k-1),R(k)}> Max{R(k-1),R(k)}

Then Elevel(k) = 100*R(k)}1),-Max{R(k

R(k)}1),-Max{R(k-R(k)}1),-R(k2),-Max{R(k

Else

Elevel(k) = 0

To determine if there is an expulsion in the examined weld, the following

conditions are checked against Elevel(k):

If Elevel(k) ≥ A

Or

If {Elevel(67)+…+ Elevel(k)} ≥ B,

where A and B are threshold parameters for expulsion detection (in our experiment A=3,

and B=14).

In order to enhance the indirect estimation of the weld status, another soft

sensing algorithm based on quality nugget estimation is introduced. Quality nugget

estimation employing Learning Vector Quantization (LVQ) classifier is designed to

provide a real time approximation of the weld nugget diameter.

Soft Sensing of Weld Quality

66

The nugget quality estimation algorithm is used to determine the number of

normal welds (normal welds are the welds within the specifications, i.e. they have

nugget diameter more than the minimum acceptable limit and exhibit no expulsion) for

the last window of p welds based on a LVQ neural network.

A two layer LVQ artificial neural network, Figure (27), is trained in a supervised

manner to approximate the mapping between the secondary resistance and the weld

nugget diameter. The LVQ model operates as a classifier that estimates whether the

nugget corresponding to a given secondary resistance pattern belongs to the class of

normal or cold (undersized) welds. The classes that the competitive layer finds are

dependent only on the distance between input vectors.

In this paper, the input P is a vector of dimension 167 (i.e. N=167), which is equal

to the number of millisecond samples in one weld after the pre-heat and cooling phase.

The number of hidden neurons is 12 while the number of output neurons is 3

corresponding to the three categories of welding status; cold, normal, and expulsion.

Consequently, the weight matrices W1 and W2 are of size (167X12) and (12X3),

respectively.

The LVQ model was trained on three, six, and five patterns of the secondary

resistance vector for cold, normal, and expulsion welds, respectively. Twelve hidden

neurons were used with a 0.01 learning rate.

67

Figure 27, LVQ network model [7]. P is the input vector of size N, W1, and S1, 2 are the weight matrices and the number of neurons in the first and second layer.

3.3 Fuzzy Logic Control Algorithm

The primary current for the next window of p welds is calculated by using a fuzzy

control algorithm relating the number of expulsion welds and number of normal welds.

Let "E" denote the number of expulsion welds detected from the expulsion

algorithm, "N" the number of normal welds detected from LVQ neural network, for the

last window of p welds, and dα the change of current.

We define the mechanism for adjusting the current gain based on the number of

expulsion and normal welds in the last window of p welds through the following set of

rules with fuzzy predicates:

If "E" is low AND "N" is low THEN αd = Pa

If "E" is medium AND "N" is low THEN αd = Ng/2

If "E" is high AND "N" is low THEN αd = Ng

If "E" is low AND "N" is medium THEN αd = Pa/2

If "E" is medium AND "N" is medium THEN αd = Ng/4

If "E" is high AND "N" is medium THEN αd = Ng/2

68

If "E" is low AND "N" is high THEN αd = Pa/4

If "E" is medium AND "N" is high THEN αd = Ng/8

If "E" is high AND "N" is high THEN αd = Ng/4

In the rules above low, medium, and high are fuzzy subsets defined on the [0, p]

universe for the number of expulsions "E", and the number of normal welds "N” (Figure

28). Ng and Pa are constants (fuzzy singletons) defining positive, negative, change of

the current gain.

The first three fuzzy rules deal with the case where the number of normal welds

“N” in the last window is low. Based on the number of detected expulsions three

alternative strategies for changing current level are considered:

• If the number of expulsions is low, it is reasonable to think that the state of the

welds is close to the cold welds status. Hence, it is necessary to increase

gradually the amount of current. This is done by modifying the change of

current αd .

• If the number detected expulsions is medium or high, it is reasonable to think that

the state of the welds is close to the expulsion state. Hence, it is necessary to

decrease gradually the amount of current (the amount is different in case of high

vs. medium number expulsions). This is done by modifying the change of the

current αd .

When the number of normal welds in the previous window is medium, the

strategies for adjusting the current level are as follows:

• It is reasonable to expect when we have low expulsion detection that the welds

state is approaching a cold weld. Therefore, the level of current should be

69

increased gradually. This is done by modifying the change of current αd . Note

that the amount of increase when “N” is medium ( αd = Pa/2) is less than the

case when “N” is low ( αd = Pa).

• The next case deals with medium expulsion rate, i.e. the welds state is close to

the expulsion status. This requires a gradual reduction of the current. This is

done by modifying the change of the current αd .Note that the amount of

decrease when “N” is medium ( αd = Ng/4) is less than the case when the “N” is

low ( αd = Ng/2).

• The last case appears when the expulsion rate is high. In this case the level of

current should be lowered dramatically to minimize the number of expulsions.

This is also done by modifying the change of current αd . Note that the amount

of decrease when “N” is medium ( αd = Ng/2) is less than the case when the “N”

is low ( αd = Ng).

The last three fuzzy rules consider high level of normal welds, i.e. satisfactory

weld quality. Their corresponding control strategies are:

• If we have low expulsion detection, the state of the welds will be close to the cold

weld status. Therefore, current level should be increased to prevent potential

cold welds. This is also done by modifying the change of current αd . Note that

the amount of increase when “N” is high ( αd = Pa/4) is less than both previous

cases, i.e. when “N” is low ( αd = Pa) and when “N” is medium ( αd = Pa/2).

• If we have medium expulsion detection, it is reasonable to consider that the state

of the welds is close to the expulsion welds status. Therefore the current level

70

should be decreased gradually. This is done by modifying the change of the

current αd . Note that the amount of decrease when “N” is high ( αd = Ng/8) is

less than both cases when “N” is medium ( αd = Ng/4) or when “N” is low ( αd =

Ng/2).

• In the last case, when the expulsion detection is high, the level of the current

should be significantly decreased. This is done by modifying the change of the

current αd . Note that the amount of decrease when “N” is high ( αd = Ng/4), is

less than both cases when “N” is medium ( αd = Ng/2) or when “N” is low ( αd =

Ng).

Applying the Simplified Fuzzy Reasoning algorithm [65], we obtain an analytical

expression for the change of the current αd depending on the rates of expulsion welds

“E” and normal welds “N” as follows:

∑ ∑∑ ∑

∀ ∀

∀ ∀

Δ

=

i jji

i jjiji

yx

yx

d)().(

).().( ,

νμ

νμ

α

where:

μi: linguistic value of the expulsion weld {low, medium, high}.

νj: linguistic value of the normal weld {low, medium, high}.

x: number of expulsion welds in the previous window detected from expulsion

algorithm.

y: number of normal welds in the previous window detected from LVQ.

71

)(xμ : firing level for the expulsion membership function

)(yν : firing level for the normal membership function

:, jiΔ amount of increment/decrement when the linguistic value of expulsion welds

is “i” and the linguistic value of normal welds is “j”.( for example, if the linguistic value of

the expulsion welds is high and the linguistic value of the normal welds is low then

glowhigh N=Δ , , where Ng negative value determines the change of the current αd )

A triangular shape membership function is used in the fuzzy control scheme,

Figure (28), where this type of membership function depends on three scalar

parameters a, b, c as given by:

Figure 28, Membership functions for 'E' the number of expulsion welds and “N” the number of normal welds in the last 'p' welds

⎪⎪⎪

⎭

⎪⎪⎪

⎬

⎫

⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

≤

≤≤−−

≤≤−−

≤

=

xc

cxbbcxc

bxaabax

ax

cbax

,0

,0

),,,;(μ

72

The parameters “a” and “c” locate the "feet" of the triangle and the parameter “b”

locates the peak.

The new target current (Inew) for the next window of p welds will be:

Inew =Iold + αd Iold

where Iold is the current in the previous window of p welds and dα is the change of the

current from fuzzy control algorithm.

3.4 Experimental Setup and Results

Proposed Intelligent Constant Current Controller algorithm was implemented in

Matlab and was experimentally tested in a supervisory control mode in conjunction with

an MFDC Constant Current Controller. Four sets of experiments were performed as

follows. The first group of tests (with/without sealer) was performed using the proposed

Intelligent Constant Current Controller. The second group (with/without sealer) was

carried out by using a conventional stepper mode. The sealer was introduced to

simulate one of the typical disturbances in a plant environment. The schematic of an

MFDC Welder [66] is shown in Figure (29). The experimental setup is illustrated in

Figure (30). Welding machine capacity is 150 KVA, with 680 lb welding force provided

from a pneumatic gun. HWPAL25 electrode type with 6.4 mm face diameter is used.

Welding time used is 233 milliseconds with 11.2 KA as initial input secondary current

from a typical welding standard schedule.

73

Figure 29, Schematics of MFDC Welder [53]


74

Each group of tests consists of sixty coupons, i.e. 360 welds (for each test

without sealer), and ten coupons, i.e. 60 welds (for each test with sealer) with two metal

stacks for each coupon are used for each test. Both tests involved welding 2.00 mm

gage hot tip galvanized HSLA steel with 0.85 mm gage electrogalvanized HSLA steel.

Tables (21 and 22) show the mechanical properties and element analysis for the tested

materials. Coupon dimensions used for testing are (1"×12") with 6 welds on each

coupon with the anchor weld as the first weld.

Thirty six coupons (216 welds) without a sealer between sheet metal and ten

coupons (60 welds) with a sealer for each group of tests were examined. Cold and

expulsion welds were checked visually in each coupon.

dressing was performed.


Material Type 0.85 mm gage, HSLA, electrogalvanized


0.2% Yield (MPa) 234 406



38 31

The length of the moving window in the Intelligent Constant Current Controller

algorithm was p = 10, i.e. the soft sensing of expulsion and normal welds was

performed on a sequence of 10 consecutive welds. The negative and positive

consequent singleton values in the rule-base of the fuzzy control algorithm were set at

Ng= -0.09 and Pa= +0.07.

75

In the stepper mode test, an increment of one ampere per weld was used as a

stepper for this test. The initial input current was set at 11.2 kA for all tests, with no

stabilization process to simulate the actual welding setup conditions in the plant after tip




Carbon 0.01 0.09 Manganese 0.20 0.46

Phosphorous 0.02 0.01 Sulfur 0.01 0.01 Silicon <0.03 0.03 Copper 0.01 0.08 Nickel 0.02 0.03

Chromium 0.03 0.07 Vanadium <0.01 0.02

Molybdenum <0.01 0.01 Aluminum 0.05 0.02 Titanium <0.01 <0.01

Tin <0.01 <0.01 Iron Base Base

Intelligent Constant Current Control and Stepper Based Control without Sealer

Figure (31) shows the weld secondary current generated by the Intelligent

Constant Current Control algorithm without sealer. It can be seen that at the beginning

of the welding process, there were a couple of cold welds, so the fuzzy control scheme

increased the current gradually until expulsion began to occur. When expulsion was

identified by the soft sensing algorithm, the fuzzy control algorithm began to decrease

the current level until expulsion was eliminated and normal welds were estimated again.

After that it continued to increase the current until expulsion occurred again and so on.

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Figure 31, Secondary current using the fuzzy model

It can be concluded from the test above that the secondary current in the

intelligent control scheme was responding to the weld status; in case of expulsion

welds, the secondary current was decreased, and in case of cold welds, the secondary

current was increased. Thus, the fuzzy control scheme was able to adapt the secondary

current level to weld state estimated by the soft sensing algorithms.

Figure (32) shows the spot secondary current in the case of conventional stepper

mode. The weld secondary current was set to a constant value at the beginning of the

test, and then an increment of one ampere per weld was used as a stepper to

compensate for the increase in electrodes diameter (mushrooming of the electrode). It

can be seen that there were a couple of cold welds at the beginning of the test, followed

by a couple of normal welds, and then expulsion welds were dominant until the end of

the test.

77

Figure 32, Secondary current for the stepper model without sealer

It can be concluded from the test above that the secondary current in the stepper

mode was too aggressive towards the end of the welding process, therefore expulsion

welds occurred. While in the beginning of the stepper the secondary current was not

enough therefore cold welds occurred. Therefore, the stepper mode doesn’t really adapt

the current to the actual weld state at the beginning or at the end of the welding

process.

Tables 23 and 24 show the number of expulsion and cold welds for the Intelligent

Constant Current Control algorithm versus the conventional stepper mode

implementation. As expected the number of expulsion welds in the stepper mode

(98/216=45.4%) is higher than the number of expulsion welds in the fuzzy control

scheme (68/216=31.5%). It can also be seen that the number of cold welds in the fuzzy

78

control scheme test (31/216=14.4%) was less than the number of cold welds in the

stepper mode (44/216 =20.4%).

Table 23, Number of expulsion welds for the fuzzy control scheme, and the conventional stepper mode without sealer

Number of expulsion welds using fuzzy scheme

Number of expulsion welds using stepper mode

68/216=31.5% 98/216=45.4%

Table 24, Number of cold welds for the fuzzy control scheme, the stepper, and the no stepper modes without sealer

Number of cold welds using fuzzy scheme

Number of cold welds using stepper mode

31/216=14.4% 44/216=20.4%

Intelligent Constant Current Control and Stepper Based Control with Sealer

It is a common practice in the automotive industry to intentionally introduce

sealer material between the two sheet metals to be welded. The purpose of this sealer

is to prevent water from collecting between the sheets and in turn reduce any potential

corrosion of the inner surface of sheet metals. However, the sealer creates problems for

the spot welding process. In particular, the sealer increases the resistance significantly

between the two sheet metals to be welded. When the welding process starts, high

current will be fired, which is faced by high resistance (because of the sealer) in the

desired spot to be welded, that will prevent the current to flow in that direction. The

other alternative direction for this current is to flow in a direction with less resistance;

this is what is known as shunting effect. Shunting effect produces cold welds, or at least

small welds, which will cause a serious problem to the structure.

Figure (35) shows the spot secondary current for the Intelligent Constant Current

Control algorithm with sealer. It demonstrated a performance similar to the case with no

79

sealer – increasing/decreasing of the current level to adapt to the estimated

cold/expulsion welds.

Figure 33, spot secondary current for the fuzzy control scheme with sealer

Figure (36) shows the spot stepper mode secondary current in the presence of

sealer. The weld secondary current was set to a constant value at the beginning of the

test with subsequent increments of one ampere per weld. It can be seen that the cold

welds were dominant until just before the end of the test. There were a couple of

normal welds towards the end of the test. No expulsion welds occurred in this test.

Apparently, the secondary current was not enough to produce cold welds. Thus, using

stepper mode does not adapt the secondary current according to the weld status.

80

Figure 34, spot secondary current for the stepper mode with sealer

Table 25, Number of expulsion welds for the fuzzy control scheme, the stepper, and the no stepper modes with sealer

Number of expulsion welds using fuzzy model

Number of expulsion welds using stepper model

3/60=5% 0/60=0.0%

Table 26, Number of cold welds for the fuzzy control scheme, the stepper, and the no stepper modes with sealer

Number of cold welds using fuzzy model

Number of cold welds using stepper model

14/60=23.3% 43/60=71.7%

Tables 25 and 26 compare the number of expulsion and cold welds for the

Intelligent Constant Current Control algorithm and the conventional stepper mode

implementation in the case of sealer. The number of expulsion welds in the fuzzy

81

control scheme test (3/60=5%) is higher than the number of expulsion welds in the

stepper mode test (0/60=0.0%).

Once again, the number of cold welds in the fuzzy control scheme

(14/60=23.3%) is less than the number of cold welds in the conventional stepper mode

test (43/60=71.7%).

3.5 Conclusions

In this chapter, an intelligent algorithm was proposed for adapting the current

level to compensate for electrode degradation in resistance spot welding. The algorithm

works as a fuzzy logic controller using a set of engineering rules with fuzzy predicates

that dynamically adapt the secondary current to the state of the weld process. A soft

sensor for indirect estimation of the weld quality employing an LVQ type classifier was

designed to provide a real time approximate assessment of the weld nugget diameter.

Another soft sensing algorithm was applied to predict the impact of the current changes

on the expulsion rate of the weld process. By keeping the expulsion rate just below a

minimal acceptable level, robust process control performance and satisfactory weld

quality are achieved. The Intelligent Constant Current Control for Resistance Spot

Welding was implemented and experimentally validated on a Medium Frequency Direct

Current (MFDC) Constant Current Weld Controller.

Results were verified by benchmarking the proposed algorithm against the

conventional stepper mode constant current control. In the case when there was no

sealer between sheet metal, it was found that the proposed intelligent control scheme

82

reduced the number of expulsion welds and the number of cold welds by 44% and 29%

respectively, when compared to using the stepper mode.

In the case when there was a sealer type disturbance, the proposed control

algorithm once again demonstrated robust performance reducing the number of cold

welds by 67% compared to the stepper mode, while increasing the number of expulsion

welds by only 5%.

It can be concluded that the Intelligent Constant Current Control Algorithm is

capable of successfully adapting the secondary current level according to welds state

and to maintain a robust performance. An alternative version of the algorithm that is

applicable to the problem of supervisory control of the weld level in the Constant Heat

Control Algorithm is under development.

83

CHAPTER 4

ELECTRODE TIP DRESSING DETECTION BY USING FUZZY C–MEANS

CLUSTERING ALGORITHM IMPLEMENTED IN A HIERARCHAL FASHION

Electrode plays a major role in resistance spot welding process by transmitting

the mechanical force and the electrical current to the work piece to be welded. Recently,

Zinc sheet metal coated steel has been widely used in the automotive industry and

others to improve the corrosion resistance in auto body constructions. However, one of

the major concerns of using the coated sheet metal is that the electrode life can be

significantly shorter than the bare (uncoated) sheet metal.

In order to decrease the effect of coating on the electrode performance (i.e.

reduce the mushrooming effect), tip dressing is done frequently on the electrode;

usually from 10 to 15 times in the auto industry with the assumption that the tip dressing

is done properly. This assumption can lead to low quality in successive welds if the tip

dressing is not done properly (or not done at all).

In this chapter, a fuzzy C-mean clustering algorithm implemented in a hierarchal

fashion is used for on line detecting the electrode health condition after the electrode tip

dressing is performed.

4.1 Introduction

Resistance spot welding is one of the primary means of joining sheet metals in

the automotive industry and others. The demand for improved corrosion resistance has

led the automotive industry increasingly to use zinc coated sheet metals in auto body

constructions. However, one of the major concerns associated with welding coated

84

sheet metals is that the electrode life can be significantly shorter than that of welding

uncoated (bare) sheet metals.

Electrode tip growth has recently been suggested as the dominant process that

determines electrode life when resistance spot welding coated steels. Researchers [20,

67, 68] have shown that the deposition of copper onto the surface of the spot weld

results in a potential net loss of material from electrode face. Measurements of the

decrease in electrode length, coupled with the depth of alloy layers formed when

welding the various coated steels, have been used to obtain a total campaign life [69].

Holiday’s approach [69] for estimating the electrode face diameter from

measuring the decrease in electrode lengths is an offline technique, besides it is

inapplicable solution for industry.

A Mathematical model has been constructed to predict the electrode face

diameter at various stages of electrode life; the model relates the electrode face

evolution process to electrode design and welding parameters such as welding current,

electrode force [70].

Lu and Dong [70] built their model on an assumption that the welding current and

the electrode force are constant during welding, whereas in reality, the electrode force

and the welding current are changing due to the electrode growth.

Improvements of electrode life are possible through the application of current

stepper techniques [60]; the most significant improvements in welding performance

were obtained when the current was increased as a function as the electrode

degradation.

85

The stepper techniques proposed by William et al [60] to compensate for the

electrode degradation, depends on the assumption that the electrode area is an easy

measurable parameter. It is also reported [59] that their approach resulted in too rapid

of growth in the electrode diameter

Matejec and Zelenak [71] demonstrate the benefits of electrode dressing when

extending the electrode life up to 30,000 welds for hot dip zinc coated steel. In this work,

8.0 kA used as initial current, without using any current stepper, the electrodes were

dressed every 20 -30 welds.

In real world application, dressing can only occur between loads. These loads

have different welds capacity, which will make Matejec and Zelenak [71] approach

inapplicable.

Ganowski and Williams [72] were able to double the electrode life by

incorporating a 2-3 mm extension to the tip of a conventional truncated cone electrode

when welding hot dip zinc coated steel.

During the electrode life, tip dressing is repeated several times (usually 10 to 15

times), and there is no criteria to determine the electrode tip dressing condition. In this

chapter, we propose a fuzzy C-mean clustering algorithm implemented in a hierarchal

fashion as a novel way for on line detection of the electrode tip dressing health

condition.

The following sections are organized as follows: Mechanisms of the electrode

growth; it contains a description of the factors that causes the electrode growth.

Electrode tip dressing; it mentions the benefits obtained from electrode tip dressing and

techniques of electrode tip dressings. The fuzzy C-mean clustering algorithm

86

implemented in a hierarchal fashion; it describes the purpose of this algorithm, and

explains of how this algorithm works. Experimental setup; we will explain how the tests

were performed, the material and the machine used in the experiment. The results; we

will present the results obtained from applying this algorithm on Constant heat control

(CHC) and Constant current control (CCC). Conclusions; we will summarize the steps of

the algorithm and compare the results of CHC and CCC.

4.2 Mechanisms of the Electrode Growth

Holiday et al [69] examined the electrode wear mechanisms of three different

alloys steel; Zn Al coated steel, hot dip zinc coated steel, and galvannealed coated

steel. It had been suggested that the electrode growth can be due:

1. Deformation and flow of unalloyed material to the tip periphery causing the

formation of “wings”, this process has been traditionally referred to as

mushrooming.

2. Alloy product (i.e. alloy migrating from the coating substrate to the electrode)

and/or zinc pickup at the electrode tip periphery can result in an increase of the

electrode diameter.

3. Any reduction in length of the electrode as a consequence of the wear process

will also cause an increase in electrode diameter (this length reduction may arise

from both the removal of material from the electrode face and a reduction in

length due to mushrooming).

In case of hot dip zinc coated steel (the most common type of coating used in the

OEM), the rate of the growth approximated to a two stages processes; growth rate

which was initially high, and then decreased as the number of welds made increased.

87

In order to determine the dominate factor of growth rate in zinc coated steel, they

performed metallurgical examination for both the welded sheet surface beneath the

welding electrodes, and the electrodes themselves.

The metallurgical examination of percentage of copper in the welded sheet

surface beneath the welding electrodes revealed that the amount of copper increased

initially (up to 100 welds) then decreased. This observation can be related to the

reduction in length of the electrode factor.

On the other hand, the metallurgical examination of cross section of the electrode

indicated extensive deformation of the underlying electrode material, which resulted in

the formation of the large wings at the periphery of the electrode tip. Also there was a

presence of alloy product in the electrode wings.

In summary holiday et al [69] concluded that in case of hot dip zinc coated steel,

the length reduction account for up to 50% of the tip growth, with the deformation of

unalloyed electrode material and alloy product over layers accounting for the remainder.

4.3 Electrode Tip Dressing

The total electrode life can be significantly increased by using tip dressing (i.e. for

hot dip zinc coated steel the electrode life is approximately 2,000 welds, while the

electrode life can be extended to approximately 20,000 welds when using carefully

controlled electrode tip dressing procedures). The limiting factor controlling electrode life

is the total amount of material which is available and which can be safely removed

without affecting the mechanical and\or thermal properties of the electrode. This is

governed by the distance between electrode face and bottom of the water cooling

channel of the electrode.

88

Some of the issues concerning the electrode tip dressing are whether to entirely

remove the alloy layer which builds up at the electrode tip surface or to simply machine

back to the original dimensions so as to remove excessive built up of the layer, and/or

whether to remove the alloy layer from the electrode periphery only, or to remove the

alloy layer from the periphery and the electrode tip face.

Regarding removing the alloy layer at the electrode tip surface, or machining

back to the original dimension, Holiday [69] showed that when welding 1mm hot dip zinc

coated steel, the electrode growth trends clearly to a two stages process when dressing

back to the original diameter. Primary stage had a growth rate between 2.5mm/1000

welds and 2.6mm/1000 welds and occurred up to 400 welds approximately, while

second stage had a growth rate between 0.45mm/1000 welds and 0.50mm/1000 welds

and occurred after the first 400 welds. While repeating the same test with removing the

alloy layer which builds up at the electrode tip surface only, the transition between the

primary and the secondary stages of the electrode growth was less clear.

It was concluded from this work that the best option would be to dress the

electrodes so as to return the electrode diameter to its original diameter without

removing the entire alloy build up at the surface, with only burnishing of the surface

alloy layer. This resulted in a more consistent electrode/sheet resistance and a lower

overall rate of growth.

On the other hand, Holiday [69] reported that the growth rate of the electrode

when removing the alloy layer from periphery only is similar to the growth rate when

removing the alloy layer from periphery and the tip electrode face.

89

4.4 Fuzzy C–Means Clustering Algorithm Implemented In a Hierarchal Fashion

The fuzzy C-mean clustering algorithm implemented in a hierarchal fashion is

used for on line detecting the electrode health condition after the electrode tip dressing.

The fuzzy C-mean clustering algorithm implemented in a hierarchal fashion consists

mainly from two modes, training and validation.

In order to explain the fuzzy C-mean clustering algorithm implemented in a

hierarchal fashion, lets’ assume that the test will begin with a new cap, and the counter

will be reset by the operator (or the robot) when electrode tip dressing occurs.

In the training mode, Figure (38), weld data (cycle resistance or cycle voltage) is

stored from the first weld to the weld that follows the first electrode tip dressing (or use

the weld data from the first weld that occurred after the first electrode tip dressing until

at least the first weld that occurred after the second electrode tip dressing). Then the

fuzzy C-mean clustering algorithm (see appendix B) is used to separate the weld

training data into two clusters. One of the new clusters is checked if it contains the first

weld and the weld after the electrode tip dressing. If this condition is not satisfied on the

examined new cluster, the fuzzy C-mean clustering algorithm will be stopped, and the

training mode will be finished. On the other hand, if this condition is satisfied on the

examined new cluster, the new cluster which has the first weld and the weld after the

electrode tip dressing will be moved to the second level, while the other cluster will be

tagged as the first cluster in level one.

In the second level, the fuzzy C-mean clustering algorithm will be used again to

separate the weld data coming from the first level (the weld data in the cluster that has

the first weld and the weld after the electrode tip dressing) into two clusters. One of the

new clusters is checked if it contains the first weld and the weld after the electrode tip

90

dressing. If this condition is not satisfied on the examined cluster, the fuzzy C-mean

clustering algorithm will be stopped, and the training mode will be finished. On the other

hand, if this condition is satisfied on the examined cluster, the cluster which has the first

weld and the weld after the electrode tip dressing will be moved to the third level, while

the other cluster will be tagged as the second cluster in level two.

The following clustering levels will follow the same sequence as the pervious

clustering level.

It should be mentioned that when the fuzzy C-mean clustering algorithm stopped

in certain level (i.e. when the first weld and the weld after the electrode tip dressing are

classified in different clusters at the same level), the weld data at the this level will be

discarded, and the two clusters in previous level will be tagged as the last two cluster.

After the training mode is finished, the number of clusters obtained will be equal

to the number of levels plus one, and the last cluster will contains the first weld and the

weld after the electrode tip dressing, Figure (39). A threshold should be established

based on the size of the clusters; usually the clusters deformed towards the end of the

hierarchal algorithm will have smaller sizes comparable to the clusters deformed

towards the top of the hierarchal.

After the training mode is finished, the evaluation mode begins with classifying

the new weld data to one of the clusters in each level in a hierarchy faction also. For

example, the new weld data will be classified in the first level to the cluster that has the

minimum distance between the centers of the clusters (obtained from training mode)

and new weld data. If the new weld data is classified to the cluster that has the first weld

and the weld after the electrode tip dressing, the new weld will be moved to the next

91

level, and the fuzzy C-means clustering algorithm will be used again to classify the weld

data. On the other hand, if the new weld data is not classified to the cluster that has the

first weld and the weld after the electrode tip dressing, the evaluation mode will be

terminated, and the new weld will be classified to the that cluster.

The following clustering levels will follow the same sequence as the pervious

clustering level.

Figure 35, Fuzzy C-means clustering Algorithm implemented in a hierarchal fashion for tip dressing quality detection

4.5 Experimental Setup

Two set of experiments; Constant Heat Control (CHC) and Constant Current

Control (CCC), were performed on Alternating current (AC) welding controller, Figure

(40).

92

Figure 36, Tip dressing hierarchy fuzzy clustering

There are two types of resistance welders: Alternating current (AC) and direct

current (DC). A DC resistance welding controller provides the advantage that the

current supplied to the weld can be controlled within stringent limits. However there are

two major disadvantages: the equipment required is expensive and the electrodes wear

out quickly because current flows in one direction only during welding. In contrast, an

AC resistance welding controller provides the advantages that the equipment required is

inexpensive and the electrodes wear out very slowly. However, a disadvantage is that

the current supplied to the weld can be controlled only within fairly loose time [57].

93

Figure 37, Alternating current controller (AC) schematic diagram

The measurement setup is shown in Figure (41). Welding machine capacity is

180 KVA, with 680 lb welding force provided from servo gun. HWPAL25 truncated

electrode type with 6.4 face diameter is used. Welding time used is 14 cycles with 10.5

KA as initial input secondary current with incremental stepper 1 ampere per weld for

constant current control.

In Constant Heat Control (CHC), total heat per unit volume is used to adjust the

welding power to optimum value to consistently achieve sturdy welds. Total heat per

unit volume required to satisfactory weld the workpieces is calculated from total

thickness of the workpieces and welding time. From this calculated total heat per unit

volume, specific heat per unit time is calculated. The CHC adjusts welding current,

Figure (42), to an optimum value required to produce the total heat per unit volume, see

chapter 2 for more detail [55]. On the other hand, in Constant Current Control, the

secondary current during the welding time is constant for each weld, Figure (43). The

secondary current is changing from weld to weld according to a stepper; usually one

ampere per weld, to compensate for the degradation of the electrode.

94


95

Two sheet metal stack up; 2.00 mm gage hot tip galvanized HSLA steel with 0.85

mm gage electrogalvanized HSLA steel were used for both tests.

Figure 39, Secondary current profile for constant heat control (CHC)

Figure 40, Secondary current profile for constant current control (CCC)

96

Tables (27 and 28) show the mechanical properties and element analysis for the

tested materials, while tables (29 and 30) show the element analysis and coating weight

for coatings substrate.


Material Type

0.85 mm gage, HSLA, electrogalvanized


0.2% Yield (MPa) 234 406



38 31




Carbon 0.01 0.09 Manganese 0.20 0.46 Phosphorous 0.02 0.01 Sulfur 0.01 0.01 Silicon <0.03 0.03 Copper 0.01 0.08 Nickel 0.02 0.03 Chromium 0.03 0.07 Vanadium <0.01 0.02 Molybdenum <0.01 0.01 Aluminum 0.05 0.02 Titanium <0.01 <0.01 Tin <0.01 <0.01 Iron Base Base

97

Table 29, Element analysis of the coating substrate (weight percent)



Aluminum 0.005 1.0 Nickel 0.065 <0.001 Zinc Balance Balance

Table 30, Coating weight

Material Coating Weight (g/m2) 0.85 mm gage, HSLA, electrogalvanized 0.70/0.64 2.00 mm gage, HSLA, hot dip galvanized 0.85/1.35

The test were performed in coupons, each coupon had 6 welds (including the

anchor weld), Figure (44). Fourteen bathes for each type of controller performed;

thirteen of them had 300 welds each, and the other left had 600 welds. After each

batch, tip dressing was performed, so total of eleven tip dressing performed in each test

Figure 41, Coupon used in CHC and CCC test

98

4.6 Results

Two sets of experiments using the Constant heat control (CHC) and the Constant

current control (CCC) were conducted to validate the fuzzy C-mean clustering algorithm

implemented in a hierarchal fashion. Using the same welding data from both controllers,

Principal Components Analysis (PCA) is used to reduce the dimension of the welding

data.

Constant heat control (CHC)

As mentioned before, fourteen batches were performed by using Constant heat

control (CHC); thirteen of them had 300 welds, while one had 600 welds. Electrode tip

dressing was performed after each batch, and the counter was being reset when the

electrode tip dressing was performed, Figure (45).

0 1000 2000 3000 4000 50000

100

200

300

400

500

600

700

Counter

Num

ber o

f Wel

ds

Figure 42, counter shows number of welds on each batch for CHC test

99

Figure (46), shows the result of the fuzzy C-mean clustering algorithm for the

training weld data when using the constant heat control CHC. Cycle secondary voltage

vector of length 24 for each weld data is used as the input for the algorithm. Two welds

performed after electrode tip dressing were used for training.

50 100 150 200 250 300 350 400 4501

2

3

4

5

6

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

2

7

8

38

190

229

Weld after tip dressing in the last cluster

Figure 43, Number of clusters obtained from training mode (6 clusters) for CHC.

Table 31, shows the number of clusters and their sizes from the training mode,

which include the welds after the electrode tip dressing in the last cluster. It can be

noticed that clusters number 4, 5, and 6 had small number of welds comparable to other

clusters, therefore a threshold can be set after cluster number 3 (i.e. in the evaluation

mode, if the weld after the tip dressing is classified to any cluster below cluster number

three, the alarm should be fired, and redressing or other actions should be considered).

100

Table 31, Clusters obtained from the training welds for CHC

Cluster No 1 2 3 4 5 6

Size 229 190 38 8 7 2


evaluation of weld data when using the constant heat control CHC. In the evaluation

mode, clustering of the weld data to one of the six clusters obtained from the training

mode was performed. Eleven welds performed after electrode tip dressing were used

for validation.

500 1000 1500 2000 2500 3000 3500 4000 45001

2

3

4

5

6

7

Weld Sequence

Clu

ster

Num

ber


Clu

ster

Siz

e

76

70

342

2346

874

821

Figure 44, Clustering of weld data for CHC test

It can be seen clearly that ten out of eleven welds performed after electrode tip

dressing belongs to the last cluster (cluster number 6), while the weld performed after

the last electrode tip dressing belongs to the previous cluster (cluster number 5).

101

In conclusion, all of the welds performed after the electrode tip dressing were

classified to clusters higher than cluster number three (the threshold), therefore all of

the eleventh electrode tip dressings were performed properly.

Table 32, shows the number of clusters and their sizes from the evaluation mode

for CHC test. It can be noticed that the last two clusters (cluster 5 and 6) which contain

the welds performed after the electrode tip dressing had small sizes comparable to the

remaining clusters.

Table 32, Size of the clusters obtained from the evaluation mode for CHC

Cluster No 1 2 3 4 5 6

Size 821 847 2346 342 70 76

Constant Current Control (CCC)

Fourteen batches were performed by using Constant current control (CCC);

thirteen of them had 300 welds, while one had 600 welds. Electrode tip dressing was

performed after each batch, and the counter was being reset when the electrode tip

dressing was performed, Figure (48).


training weld data when using the constant current control (CCC). Cycle secondary

resistance vector of length 28 for each weld data is used as the input for the algorithm.

Two welds performed after electrode tip dressing were used for training.

102


with twenty four welds in the last cluster, which contains the two welds after the

electrode tip dressing.

0 1000 2000 3000 4000 50000

100

200

300

400

500

600

700

Counter

Num

ber o

f wel

ds

Figure 45, counter shows number of welds on each batch for CCC test

Table 33, Clusters obtained from the training welds for CCC

Cluster No 1 2 3 4

Size 262 146 42 24

It can be noticed that clusters number 3, and 4, had small number of welds

comparable to other clusters, therefore a threshold can be set after cluster number 2

(i.e. in the evaluation mode, if the weld after the tip dressing is classified to any cluster

103

below cluster number two alarm should be fired, and redressing or other actions should

be considered).

50 100 150 200 250 300 350 400 4501

2

3

4

5

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

262

146

42

24


Figure 46, Number of clusters obtained from training mode (4 clusters) for CCC.


evaluation of weld data when using the constant current control CCC. In the evaluation

mode, clustering of the weld data to one of the four clusters obtained from the training

mode was performed. Eleven welds performed after electrode tip dressing were used

for validation.

It can be seen clearly that all the eleven welds performed after electrode tip

dressing belongs to the last cluster (cluster number 4).

104


classified to clusters higher than cluster number two (the threshold), therefore all of the

eleventh electrode tip dressings were performed properly.

500 1000 1500 2000 2500 3000 3500 4000 45001

2

3

4

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

973

1435

1107

1016


Figure 47, Number of clusters obtained from validation mode (4 clusters) for CCC

Table 34 shows the number of the clusters and their sizes from the evaluation

mode for CCC test. It can be noticed that the size of the four clusters are very close to

each other. The last cluster which contains the welds performed after the electrode tip

dressing had the smallest size comparable to the remaining clusters.

Table 34, Size of the clusters obtained from the evaluation mode for CCC

Cluster No 1 2 3 4

Size 1016 1107 1435 973

105

Principal Component Analysis (PCA) with Constant heat control (CHC)

Principle Components Analysis (PCA) is a method for dimension reduction based

on finding the eigenvectors of the covariance matrix (or the correlation matrix) for the

initial random variables. Principle components themselves are particular linear

combination of the initial random variables. (more information about PCA can be

obtained in reference [73]).

Figure (51), shows the Scree plot for the same training welding data when using

Constant heat control. The Scree plot can be used to determine the number of principle

components that should be used as input for the algorithm (i.e. the first four principle

components had the highest eigen values, and they account for 94.4% of the total

variance).


training weld data when using the constant heat control CHC. The first four principal

components of the cycle voltage (instead of vector of length 24) for each weld data were

used as the input for the algorithm.

On the other hand, three welds performed after electrode tip dressing were used

for training, while using the entire cycle secondary .voltage vector only two welds

performed after electrode tip dressing were used for training. It is obvious that this

increment was due to the reduction in input vector size of the algorithm.

106

5 10 15 200

100

200

300

400

Component Number

Eig

enva

lue

Figure 48, Scree plot for training CHC welding data

100 200 300 400 500 600 7001

2

3

4

5

6

Weld Sequence

Clu

ster

Num

ber


Clu

ster

Siz

e

10

21

103

208

444

Figure 49, Number and size of clusters obtained from training mode (5 clusters) for CHC when 4 principal components were used as input to the algorithm


which include the welds after the electrode tip dressing in the last cluster. It can be

107

noticed that clusters number 4, and 5 had small number of welds comparable to other

clusters, therefore a threshold can be set after cluster number 3 (i.e. in the evaluation

mode, if the weld after the tip dressing is classified to any cluster below cluster number

three, the alarm should be fired, and redressing or other actions should be considered).

Table 35, Clusters obtained from the training welds for CHC when 4 principal components were used as input for the algorithm

Cluster No 1 2 3 4 5

Size 444 208 103 21 10


evaluation of weld data when using the constant heat control CHC. The first four

principal components of the cycle voltage (vector of length 24) for each weld data were

used as the input for the algorithm. In the evaluation mode, clustering of the weld data

to one of the five clusters obtained from the training mode was performed. Ten welds

performed after electrode tip dressing were used for validation.

It can be seen clearly that all the ten welds performed after electrode tip dressing

belongs to the last cluster (cluster number 5).


classified to clusters higher than cluster number three (the threshold), therefore all of

the tenth electrode tip dressings were performed properly.

Table 36 shows the number of clusters and their sizes from the evaluation mode

for CHC test when using 4 principal components as the input for the algorithm. It can be

108

noticed that the last cluster (cluster number 4) which contains the welds performed after

the electrode tip dressing had small size comparable to the remaining clusters.

1000 1500 2000 2500 3000 3500 4000 45001

2

3

4

5

6

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

93

107

538

2153

1638


Figure 50, Clustering of weld data for CHC test when 4 principal components were used as input for the algorithm

Table 36, Size of the clusters obtained from the evaluation mode for CHC when 4 principal components were used as the input for the algorithm

Cluster No 1 2 3 4 5

Size 1638 2153 538 107 93

109

Principal Component Analysis (PCA) with Constant current control (CCC)

Principle Components Analysis (PCA) is used to reduce the dimension of the

entire input vector (i.e. the secondary resistance vector) used by the fuzzy C-mean

clustering algorithm.

5 10 15 20 250

100

200

300

400

500

600

Component Number

Eig

enva

lue

Figure 51, Scree plot for training CCC welding data

Figure (54), shows the Scree plot for the same training welding data when using

Constant current control. The Scree plot can be used to determine the number of

principle components that should be used as input for the algorithm (i.e. the first seven

principle components had the highest eigen values, and they account for 95.0% of the

total variance).


training weld data when using the constant current control CCC. The first seven

principal components of the cycle resistance (vector of length 28) for each weld data

were used as the input to the algorithm.

110

On the other hand, three welds performed after electrode tip dressing were used

for training, while using the entire cycle secondary .resistance vector only two welds

performed after the electrode tip dressing were used for training. It is obvious that this

increment was due to the reduction in input vector size of the algorithm.

100 200 300 400 500 600 7001

2

3

4

5

6

7

8

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

5

6

12

40

78

232

414

Weld after tip dressing in the last Cluster

Figure 52, Number and size of clusters obtained from training mode (7 clusters) for CCC when 7 principal components were used as input to the algorithm

Table 37, Clusters obtained from the training welds for CCC when 7 principal components were used as input to the algorithm

Cluster No 1 2 3 4 5 6 7

Size 414 232 78 40 12 6 5


which include the three welds after the electrode tip dressing in the last cluster. It can be

noticed that clusters number 5, 6, and 7 had small number of welds comparable to the

111

other clusters, therefore a threshold can be set after cluster number 4 (i.e. in the

evaluation mode, if the weld after the electrode tip dressing is classified to any cluster

below cluster number four, the alarm should be fired, and redressing or other actions

should be considered).

1000 1500 2000 2500 3000 3500 4000 45001

2

3

4

5

6

7

8

Weld Sequence

Clu

ster

Num

ber

Clu

ster

Siz

e

Weld after tip dressing in the last Cluster

287

503

18

566

793

1300

1064

Figure 53, Clustering of weld data for CCC test when 7 principal components were used as input for the algorithm


evaluation of weld data when using the constant current control CCC. The first seven

principal components of the cycle resistance (instead of entire vector of length 28) for

each weld data were used as the input for the algorithm. In the evaluation mode,

clustering of the weld data to one of the seven clusters obtained from the training mode

was performed. Ten welds performed after the electrode tip dressing were used for

validation.

112

It can be seen clearly that all the ten welds performed after electrode tip dressing

belongs to the last cluster (cluster number 7).


classified to clusters higher than cluster number four (the threshold), therefore all of the

tenth electrode tip dressings were performed properly.

Table 38, Size of the clusters obtained from the evaluation mode for CCC when 7 principal components were used as the input for the algorithm

Cluster

No 1 2 3 4 5 6 7

Size 1064 1300 793 566 287 18 503

Table 38, shows the number of clusters and their sizes from the evaluation mode

for CCC test when using 7 principal components as the input for the algorithm. It can be

noticed that the last cluster (cluster number 7) contains all the welds performed after the

electrode tip dressing had medium size comparable to the remaining clusters.

4.7 Conclusions

The fuzzy C-mean clustering algorithm implemented in a hierarchal fashion is

used for on line detecting the electrode health condition after the tip dressing. The fuzzy

C-mean clustering algorithm consists mainly from two modes, training and validation

modes. Two welds occurred after the electrode tip dressings were used for training, and

eleven welds occurred after the electrode tip dressings were used for evaluation.

Fuzzy C-mean clustering algorithm implemented in a hierarchal fashion can be

summarized in the following steps:

113

1. Store the welding data (the entire vector of cycle resistance when using constant

current control (CCC), or the entire vector of cycle voltage when using constant

heat control (CHC)) from the weld number one until at least the weld that

occurred after the first electrode tip dressing (or use the weld data from the first

weld that occurred after the first electrode tip dressing until at least the first weld

that occurred after the second electrode tip dressing).

2. Perform Fuzzy C-mean clustering in hierarchy fashion on the training data, and

store levels of clustering with the clusters centers at each level.

3. Establish a threshold based on the size of the clusters; usually the clusters

deformed towards the end of the hierarchal will have smaller sizes comparable to

the clusters deformed towards the top of the hierarchal.

4. Classify the new weld data in a hierarchy fashion, based on the minimum

distance between the new weld data and the clusters center in each level.

5. In the evaluation mode, if the weld after the tip dressing is classified to any

cluster below the threshold, alarm should be fired and/or redressing or any other

actions should be considered.

Experiments based on Constant Heat Control (CHC) and Constant Current

Control (CCC), were performed to verify the fuzzy C-mean clustering algorithm

implemented in a hierarchal fashion. The entire vector of cycle resistance when using

constant current control (CCC), or the entire vector of cycle voltage when using

constant heat control (CHC) were used as input to the algorithm.

114

Table (39) summarizes the results obtained when using the entire cycle

resistance vector in case of CCC or the entire cycle voltage vector in case of CHC, as

inputs for the fuzzy C-mean clustering algorithm. From the training mode, a threshold

established after the third cluster in case of CCC, and after the second cluster in case of

CHC. All the first welds (used for evaluation) that occurred after the electrode tip

dressings classified above the threshold for both CHC and CCC, therefore we conclude

that all the tip dressings were performed properly.

Table 39, Number and sizes of clusters obtained when using the entire cycle resistance vector in case of CCC or the entire cycle voltage vector in case of CHC, as inputs for the fuzzy C-mean clustering algorithm implemented in a hierarchal fashion

Cluster Number Constant Heat Control

(CHC) Constant Current Control

(CCC) Training mode Evaluation mode Training mode Evaluation mode

1 229 821 262 1016 2 190 847 146 1107

3 38 2346 42 1435

4 8 342 24 973

5 7 70 NA NA

6 2 76 NA NA

It can be seen that the clustering results in Constant heat control (CHC) are

different from Constant current control in the following aspects:

• Constant heat control (CHC) had more number of clusters than Constant current

control (CCC).

• The size of the clusters towards the end of the fuzzy C-mean clustering algorithm

when using Constant heat control (CHC) are smaller than the size of the clusters

115

towards the end of the fuzzy C-mean clustering algorithm when using Constant

current control (CCC).

Principal Components Analysis (PCA) is used to reduce the dimension of the

input vector to the fuzzy C-mean clustering algorithm implemented in a hierarchal

fashion. By using Scree plot, four principal components in case of CHC, and seven

principle components in case of CCC, were used as inputs to the fuzzy C-mean

clustering algorithm. Three welds occurred after the electrode tip dressings were used

for training, and ten welds occurred after the electrode tip dressings were used for

evaluation.

Table (40) summarizes the results obtained when using seven principal

components in case of CCC and four principal components in case of CHC, as inputs to

the fuzzy C-mean clustering algorithm. From the training mode, a threshold established

after the fourth cluster in case of CCC, and after the third cluster in case of CHC. All the

first welds (used for evaluation) that occurred after the electrode tip dressings classified

above the threshold for both CHC and CCC, therefore we conclude that all the tip

dressings were performed properly.

It can be seen that the clustering results in Constant heat control (CHC), when

reducing the dimension of the inputs vector by using the principal components analysis,

is different from Constant current control in the following aspects:

• Constant heat control (CHC) had lass number of clusters than Constant current

control (CCC).

• The size of the clusters towards the end of the fuzzy C-mean clustering algorithm

when using Constant heat control (CHC) are smaller than the size of the clusters

116

towards the end of the hierarchal fuzzy C-mean clustering algorithm when using

Constant current control (CCC).

Table 40, Number and sizes of clusters obtained when using seven principal components in case of CCC and four principal components in case of CHC, as inputs

for the fuzzy C-mean clustering algorithm implemented in a hierarchal fashion

Cluster Number Constant Heat Control

(CHC) Constant Current Control

(CCC) Training mode Evaluation mode Training mode Evaluation mode

1 444 1638 414 1064 2 208 2153 232 1300

3 103 538 78 793

4 21 107 40 566

5 10 93 12 287

6 NA NA 6 18

7 NA NA 5 503

117

CHAPTER 5

CONCLUSIONS AND FUTURE WORK





low carbon steel, coated steels) and thicknesses, a process with short cycle times, and

a relatively robust process with some tolerance to fit-up variations. It is favored in the

automotive industry to join steel frame and body components, where 3000-4000 spot

welds per vehicle result in 30-40 billion welds being made in cars each year in the U.S.

alone.

However, given the uncertainty associated with individual weld quality (attributed

to factors such as tip wear, sheet metal surface debris, fluctuations in power supply

etc.). A solution used extensively in the automotive industry is to over design the

number of welds needed in a vehicle by 25% or more. Such over welding, in lieu of full

control is costly, as 7.5 to 10 billion welds may not be needed. In recent years, global

competition for improved productivity and reduced non-value added activity, is forcing

companies such as the automotive OEMs to eliminate these redundant spot welds. In

order to minimize the number of spot welds and still satisfy essential factors such as

strength, weld quality must be obtained.

118

5.1 Conclusions

The problem of real time estimation of the weld quality from the process data is

one of the major issues in the weld quality process improvement. This is particularly the

case for resistance spot welding. Most of the models offered in the literature to predict

nugget diameter from the process data employ measurements such as voltage and

force and are not suitable in an industrial environment for two major reasons: the input

signals for prediction model are taken from intrusive sensors (which will affect the

performance or capability of the welding cell), and, the methods often required very

large training and testing datasets.

In order to overcome these short comings, we propose a Linear Vector

Quantization (LVQ) neural network for nugget quality classification that employs the

easily accessible dynamic resistance profile as input. The goal is to make an on-line

distinction between normal welds, cold welds, and expulsion welds. Our additional goal

is to address this task when employing two types of weld controllers: Constant Current

Controller that employs Medium Frequency Direct Current and a Constant Heat

Controller that employs Alternating Current. The results from applying the LVQ neural

network trained using very limited data collected during the stabilization process are

very promising and are reported in detail. In addition, we report very promising results

when a reduced feature set is employed for classification rather than the complete

dynamic resistance profile. The features were selected using power of test criteria.

Overall, the results are very promising for developing practical on-line quality

monitoring systems for resistance spot-welding machines.

Based on these results from Linear Vector Quantization (LVQ), an intelligent

algorithm was proposed for adapting the current level to compensate for electrode

119

degradation in resistance spot welding. The algorithm works as a fuzzy logic controller

using a set of engineering rules with fuzzy predicates that dynamically adapt the

secondary current to the state of the weld process. A soft sensor for indirect estimation

of the weld quality employing an LVQ type classifier was designed to provide a real time

approximate assessment of the weld nugget diameter. Another soft sensing algorithm

was applied to predict the impact of the current changes on the expulsion rate of the

weld process. By keeping the expulsion rate just below a minimal acceptable level,

robust process control performance and satisfactory weld quality are achieved. The

Intelligent Constant Current Control for Resistance Spot Welding was implemented and

experimentally validated on a Medium Frequency Direct Current (MFDC) Constant

Current Weld Controller.

Results were verified by benchmarking the proposed algorithm against the

conventional stepper mode constant current control. In the case when there was no

sealer between sheet metal, it was found that the proposed intelligent control scheme

reduced the number of expulsion welds and the number of cold welds by 44% and 29%

respectively, when compared to using the stepper mode.

In the case when there was a sealer type disturbance, the proposed control

algorithm once again demonstrated robust performance reducing the number of cold

welds by 67% compared to the stepper mode, while increasing the number of expulsion

welds by only 5%.

It can be concluded that the Intelligent Constant Current Control Algorithm is

capable of successfully adapting the secondary current level according to welds state

and to maintain a robust performance. An alternative version of the algorithm that is

120

applicable to the problem of supervisory control of the weld level in the Constant Heat

Control Algorithm is under development.

Another important area explored in the thesis concerning the electrode tip

dressing. Electrode plays a major role in resistance spot welding process by

transmitting the mechanical force and the electrical current to the work piece to be

welded. Recently, Zinc sheet metal coated steel has been widely used in the automotive

industry and others to improve the corrosion resistance in auto body constructions.

However, one of the major concerns of using the coated sheet metal is that the

electrode life can be significantly shorter than the bare (uncoated) sheet metal.

In order to decrease the effect of coating on the electrode performance (i.e.

reduce the mushrooming effect), tip dressing is done frequently on the electrode;

usually from 10 to 15 times in the auto industry with the assumption that the tip dressing

is done properly. This assumption can lead to low quality in successive welds if the tip

dressing is not done properly (or not done at all).

Therefore, a fuzzy C-mean clustering algorithm implemented in a hierarchal

fashion is used for on line detecting the electrode health condition after the electrode tip

dressing is performed. The fuzzy C-mean clustering algorithm consists mainly from two

modes, training and validation.

Two different types of controller; Constant heat control (CHC) and Constant

current control (CCC), were used to verify the algorithm. In both (CHC) and (CCC) tests,

all the welds occurred after the electrode tip dressings were classified correctly in

clusters above the threshold, which means that all the tip dressing were done properly.

121

Principal components Analysis (PCA) is used to reduce the dimension of the

input vector of the fuzzy C-mean clustering algorithm. The first four principal

components when using (CHC), and the first seven principal components when using

(CCC), were used as inputs for the fuzzy C-mean clustering algorithm. Again, in both

(CHC) and (CCC) tests, all the welds occurred after the electrode tip dressings were

classified correctly in clusters above the threshold, which means that all the tip dressing

were done properly. It can be concluded from these tests that type 1 error (false alarm)

for the fuzzy C-mean clustering algorithm is zero.

5.2 Recommendations for Future Work

Based on encouraging results of this research, the following directions for the

future work are recommended:

• Implementing of Linear Vector Quantization (LVQ) algorithm with the

adaptive fuzzy control scheme on Medium Frequency Direct Current

(MFDC) with Constant heat control (CHC) (MFDC with CHC is still under

development).

• Verifying Linear Vector Quantization (LVQ) algorithm when incorporating

different types of noises such as axial and radial force misalignment, gap,

and force variation.

• Verifying Linear Vector Quantization (LVQ) algorithm with the adaptive

fuzzy control scheme on different types of materials such as aluminum,

and high strength steel.

• Developing a model that will determine when the electrode needs to be

dressed. Until now, there is no model that provides the industry about

122

when they need to do tip dressing, this work will be a milestone in the area

of spot welding.

123

APPENDIX A

EXPULSION DETECTION ALGORITHM

Expulsion refers to the ejection of molten metal from the weld fusion zone during

the spot welding process. This is undesirable due to detrimental effect on weld nugget

integrity (the loss of metal from the fusion zone can reduce weld size and result in weld

porosity, which may significantly reduce the strength and durability of the welded

joints.[64]

On the other hand, in order to get the optimum strength for the weld, the input

parameters (current, time, force) need to be targeted just below the expulsion.[24]

Expulsion can be caused by four main factors; insufficient electrode force,

excessive heating, worn electrodes, and poor sheet surface condition.

During spot welding, if an excessively high welding current or long welding time

used, the nugget radius can grow larger than the electrode contact radius, and if the

electrode pressure distribution can no longer contain the molten nugget, metal is

ejected. This type of expulsion usually happens during the mid–to-late stages of weld

growth and a considerable volume of molten metal can be lost.

Improper surface conditions or worn electrodes can cause expulsion to happen

at the faying interface or at the electrode/sheet contact surface area (splash) due to

high localized current density on both places. This type of expulsion may occur at any

point during the welding cycles.[64]

Many researchers exposed to the problem of expulsion detection from different

signals (i.e. voltage, resistance, force, ultrasonic). [24, 37, 64, 74]

124

In our experiments, expulsion detection is based on a drop in the resistance as

shown in Figure (57).The current value of the resistance is compared to the minimum

value of the previous two resistances. If the difference is greater than a predetermined

threshold, expulsion flag is raised.

0 50 100 150 200 25060

80

100

120

140

160

180

Dyn

amic

Res

ista

nce

(mic

ro o

hm)

Welding Time (milli seconds)

Normal Weld

Cold Weld

Expulsion Weld

Figure 54, Dynamic resistance for cold, expulsion and good welds for MFDC constant current control

125

APPENDIX B

FUZZY C-MEANS CLUSTERING

Fuzzy C-means (FCM) is a method of clustering which allows one piece of data

to belong to two or more clusters. This method (developed by Dunn 1973 and improved

by Bezdek 1981) is frequently used in pattern recognition. It is based on minimization of

the following objective function:

Jm = 2

1 1ji

N

i

C

j

m

ijcxu −∑∑

= = ,1 ∞≤≤ m

where m is any real number greater than 1, uij is the degree of membership of xi in the

cluster j, xi is the ith of d-dimensional measured data, cj is the d-dimension center of the

cluster, and ||*|| is any norm expressing the similarity between any measured data and

the center.

Fuzzy partitioning is carried out through an iterative optimization of the objective

function shown above, with the update of membership uij by:

uij =

∑=

−

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

−

−C

k

m

ki

ji

cx

cx

1

12

1

and the cluster centers cj by:

126

cj = ∑

∑

=

=N

i

m

ij

N

ii

m

ij

u

u x

1

1.

This iteration will stop when maxij ( ) ( ){ }uu k

ij

k

ij −+1 < ε , where ε is a termination

criterion between 0 and 1, whereas k are the iteration steps. This procedure converges

to a local minimum or a saddle point of Jm.

The algorithm is composed of the following steps:

1. Initialize U=[uij] matrix, U(0)

2. At k-step: calculate the centers vectors C(k)=[cj] with U(k)

cj = ∑

∑

=

=N

i

m

ij

N

ii

m

ij

u

u x

1

1.

3. Update U(k) , U(k+1)

uij =

∑=

−

⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

−

−C

k

m

ki

ji

cx

cx

1

12

1

4. If || U(k+1) - U(k)||<ε then STOP; otherwise return to step 2.

127

REFERENCES

[1] Scanmaster, "Spot Weld Inspection in the Automotive Industry."

[2] A. W. Society, Welding Handbook, 5 ed., 1968.

[3] ASTM, "Standard Test methods for Measuring Contact Resistance for Electrical

Connections," Philadelphia, PA 1985.

[4] VOGT, 2004, p. Spot Weld Inspection

[5] Anon, "Evaluating the Integrity of Spot Weld Joints in Metal," Insight, vol. 40, pp.

778-779, NOV 1998.

[6] A. Denisov, C. Shakarji, B. Lawford, R. Maev, and J. Paille, "Spot weld analysis

with 2D ultrasonic Arrays," Journal of Research of the National Institute of

Standards and Technology, vol. 109, pp. 233-244, MAR-APR 2004.

[7] B. Doyum, "Ultrasonic Examination of Resistance Spot Welds," Non Destructive

Testing (NDT), vol. 8, 2003.

[8] H. Lee, M. Wang, R. Maev, and E. Maeva, "A study on using scanning acoustic

microscopy and neural network techniques to evaluate the quality of resistance

spot welding," International Journal of Advanced Manufacturing Technology, vol.

22, pp. 727-732, NOV 2003.

[9] V. T. Pronyakin, N. K. Rybakov, and Y. N. Panchenko, "Ultrasonic flaw detection

in the welded joints of thin-walled articles," Russian Journal of Nondestructive

Testing, vol. 33, pp. 215-218, APR 1997.

[10] D. Roberts, J. Mason, and C. Lewis, "Ultrasonic spot weld testing with automatic

classification," Science and Technology of Welding and Joining, vol. 7, pp. 47-50,

FEB 2002.

128

[11] D. R. Roberts, J. Mason, and C. Lewis, "Ultrasonic spot weld testing: attenuation

study," Insight, vol. 42, pp. 720-724, NOV 2000.

[12] B. Rooks, "Advances in resistance welding for body-in-white," Assembly

Automation, vol. 23, pp. 159-162, 2003.

[13] J. X. Tao and J. K. Hu, "Ultrasonic method for evaluation of resistance spot

welding," Progress in Natural Science, vol. 11, pp. S190-S193, MAY 2001.

[14] J. Tusek and T. Blatnik, "Ultrasonic detection of lack of fusion in spot welds,"

Insight, vol. 44, pp. 684-688, NOV 2002.

[15] H. S. Yu and B. G. Ahn, "A study on ultrasonic test for evaluation of spot

weldability in automotive materials," KSME International Journal, vol. 13, pp. 775-

782, NOV 1999.

[16] K. Asibu and C. Chien, "Investigation of Monitoring Systems for Resistance Spot

Welding," Welding Journal, 2002.

[17] A. De, O. Gupta, and L. Dorn, "An Experimental Study of resistance spot welding

in 1 mm thick sheet of low carbon steel," in Proceedings of institute of

mechanical Engineering, Part B, 1996, pp. 341-347.

[18] Dorn, Lutz , Xu, and Ping, "Influence of the mechanical properties of resistance

welding machine on the quality of the spot weld," Welding and Cutting, vol. 1, pp.

12-16, 1993.

[19] S. Gedeon , C. Ulrich, and T. Eager, "Measurement of Dynamic Electrical and

Mechanical Properties of resistance spot welds," Welding Journal, pp. 387-385,

1987.

129

[20] J. E. Gould, l. R. Lebman, and S. Holmes, "A Design of Experiment Evaluation of

Factors Affecting The Resistance Spot Weldability of High Strength Steel," in

AWS Sheet Metal Welding Conference VII, Detroit, MI, 1996.

[21] O. Hahn, L. Budde, and D. Hanitzsch, "Investigation on the Influence of the

mechanical properties of spot welding tongs on the welding process," Welding

and Cutting, vol. 1, pp. 6-8, 1990.

[22] M. J. Karagoulis, "A nuts - and Bolts Approach to the control Resistance

welding," Welding Journal, pp. 27-31, july 1994.

[23] H. J. Krause and B. Lehmkuhl, "Measuring the dynamic mechanical

characteristics of spot and projection welding machines - Measured parameters,

measuring procedures and initial results," Technical Report 1984.

[24] P. Podrzaj, I. Polajnar, J. Diaci, and Z. Kariz, "Expulsion detection system for

resistance spot welding based on a neural network," Measurement Science &

Technology, vol. 15, pp. 592-598, MAR 2004.

[25] T. Satoh, Katayama, J., and Okumura, S.,, "Effects of mechanical properties of

spot welding machine on electrode life for mild steel," International Institute of

Welding, 1988, p. 912.

[26] A. Stiebel, "Thermal Force Feedback System (TFF)," 1990.

[27] H. Chang, Y. Cho, S. Choi, and H. Cho, "A proportional -Integral Controller for

resistance spot welding using nugget expansion," ASME Journal of Dynamic

Systems , Measurement and Control, vol. 111, pp. 332-336, 1989.

130

[28] H. S. Cho and D. W. Chun, "A Microprocessor-Based Electrode Movement

Controller for Spot Weld Quality Assurance," IEEE Transactions on Industrial

Electronics, vol. 32, pp. 234-238, 1985.

[29] K. Haefner, B. Carey, B. Bernstein, K. Overton, and M. Dandrea, "Real-Time

Adaptive Spot-Welding Control," Journal of Dynamic Systems Measurement and

Control-Transactions of the Asme, vol. 113, pp. 104-112, MAR 1991.

[30] K. I. Johnson, "Resistance welding quality control techniques," Metal

Construction and British Welding Journal, vol. 5, pp. 176-181, 1973.

[31] M. Jou, C. Li, and R. Messler, "Controlling resistance spot welding using neural

network and fuzzy logic," Science and Technology of Welding and Joining, vol. 3,

pp. 42-50, 1998.

[32] S. A. Gedeon, C. D. Ulrich, and T. W. Eager, "Measurement of Dynamic

Electrical and Mechanical Properties of resistance spot welds," Welding Journal,

pp. 387-385, 1987.

[33] A. Stiebel, Ulmer, C.,Kodrack, D., and Holmes, B. B., "Monitoring and control of

spot weld operations," in SAE , International Congress and Exposition, Detroit,

Michigan, 1986, p. # 860579.

[34] C. Tsai, W. Dai, D. Dickinson, and J. Papritan, "Analysis and Development of a

Real-Time Control Methodology in Resistance Spot-Welding," Welding Journal,

vol. 70, pp. S339-S351, DEC 1991.

[35] D. N. Waller, & Knowlson, P.M.,, "Electrode separation applied to quality control

in resistance welding," Welding Journal, vol. 12, pp. 168-174, 1965.

131

[36] R. T. Wood, L. W. Bauer, J. F. Bedard, B. M. Bernstein, J. Czechowski, M. M.

Dandrea, and R. A. Hogle, "A Closed-Loop Control-System for 3-Phase

Resistance Spot-Welding," Welding Journal, vol. 64, pp. 26-30, 1985.

[37] D. Dickinson, J. Franklin, and A. Stanya, "Characterization of Spot-Welding

Behavior by Dynamic Electrical Parameter Monitoring," Welding Journal, vol. 59,

pp. S170-176, 1980.

[38] W. L. Roberts, "Resistance variations during spot welding," Welding Journal, vol.

30, pp. 1004-19, 1951.

[39] W. Savage, E. Niplles, and F. Wassel, "Dynamic contact resistance of series spot

welds," Welding Journal, vol. 57, pp. 43-50, 1978.

[40] P. H. Thornton, A. R. Krause, and R. G. Davies, "Contact resistances in spot

welding," Welding Journal, vol. 75, pp. S402-S412, DEC 1996.

[41] J. G. Kaiser, G. J. Dunn, and T. W. Eagar, "The Effect of Electrical-Resistance

on Nugget Formation During Spot-Welding," Welding Journal, vol. 61, pp. S167-

S174, 1982.

[42] S. R. Patange, T. Anjaneyulu, and G. P. Reddy, "Microprocessor-Based

Resistance-Welding Monitor," Welding Journal, vol. 64, pp. 33-38, 1985.

[43] Y. Cho and S. Rhee, "New Technology for measuring dynamic resistance and

estimating strength in resistance spot welding," Measurement Science &

Technology, vol. 11, pp. 1173-1178, 2000.

[44] S. R. Lee, Y. J. Choo, T. Y. Lee, M. H. Kim, and S. K. Choi, "A quality assurance

technique for resistance spot welding using a neuro-fuzzy algorithm," Journal of

Manufacturing Systems, vol. 20, pp. 320-328, 2001.

132

[45] S. C. Wang and P. S. Wei, "Modeling dynamic electrical resistance during

resistance spot welding," Journal of Heat Transfer-Transactions of the ASME,

vol. 123, pp. 576-585, JUN 2001.

[46] M. Jou, "Real time monitoring weld quality of resistance spot welding for the

fabrication of sheet metal assemblies," Journal of Materials Processing

Technology, vol. 132, pp. 102-113, JAN 10 2003.

[47] A. A. Denisov, C. M. Shakarji, B. B. Lawford, R. G. Maev, and J. M. Paille, "Spot

weld analysis with 2D ultrasonic Arrays," Journal of Research of the National

Institute of Standards and Technology, vol. 109, pp. 233-244, MAR-APR 2004.

[48] H. T. Lee, M. Wang, R. Maev, and E. Maeva, "A study on using scanning

acoustic microscopy and neural network techniques to evaluate the quality of

resistance spot welding," International Journal of Advanced Manufacturing

Technology, vol. 22, pp. 727-732, NOV 2003.

[49] J. X. Tao, J. K. Hu, K. Y. Zhou, Y. H. Hu, Y. H. Huang, X. Chen, and Z. Y. Pan,

"Ultrasonic method for evaluation of resistance spot welding," Progress in Natural

Science, vol. 11, pp. S190-S193, MAY 2001.

[50] W. T. C.-M. A. Stiebel, "Thermal Force Feedback System (TFF)," 1990.

[51] A. E. Kannatey and C. S. Chien, "Investigation of Monitoring Systems for

Resistance Spot Welding," Welding Journal, 2002.

[52] C. L. Tsai, W. L. Dai, D. W. Dickinson, and J. C. Papritan, "Analysis and

Development of a Real-Time Control Methodology in Resistance Spot-Welding,"

Welding Journal, vol. 70, pp. S339-S351, DEC 1991.

133

[53] M. Jou, C. J. Li, and R. W. Messler, "Controlling resistance spot welding using

neural network and fuzzy logic," Science and Technology of Welding and Joining,

vol. 3, pp. 42-50, 1998.

[54] Y. Park and H. Cho, "Quality evaluation by classification of electrode force

patterns in the resistance spot welding process using neural networks,"

Proceedings of the Institution of Mechanical Engineers, Part B (Journal of

Engineering Manufacture), vol. 218, p. 1513, 2004.

[55] H. Hasegawa and M. Furukawa, "Electric Resistance Welding System," U.S

Patent 6130369, 2000.

[56] J. Wales, "Wikipedia Free Encyclopedia ", 2003.

[57] Y. Nishiwaki and Y. Endo, "Resistance welding Controller," W. T. Corporation,

Ed. USA, April 10, 2001.

[58] W. Li and D. Cerjanec, "A comparative study of AC and MFDC resistance spot

welding," Anaheim, CA, United States, 2004, p. 99.

[59] N. T. Williams and J. D. Parker, "Review of resistance spot welding of steel

sheets Part 2 - Factors influencing electrode life," International Materials

Reviews, vol. 49, pp. 77-108, APR 2004.

[60] N. T. Williams, R. J. Holiday, and J. D. Parker, "Current stepping programmes for

maximizing electrode campaign life when spot welding coated steels," Science

and Technology of Welding and Joining, vol. 3, pp. 286-294, 1998.

[61] R. W. Messler, J. Min, and C. J. Li, "An intelligent control system for resistance

spot welding using a neural network and fuzzy logic," in IEEE Industry

Applications Conference Orlando, FL, USA, Oct. 8-12, 1995, pp. 1757-1763.

134

[62] X. Chen and K. Araki, "Fuzzy adaptive process control of resistance spot welding

with a current reference model," in Proc. of the IEEE International Conference on

Intelligent Processing Systems, ICIPS

Beijing, China, 1998, p. 190.

[63] S. Lee, Y. Choo, T. Lee, M. Kim, and S. Choi, "A quality assurance technique for

resistance spot welding using a neuro-fuzzy algorithm," Journal of Manufacturing

Systems, vol. 20, pp. 320-328, 2001.

[64] M. Hao, K. A. Osman, D. R. Boomer, C. J. Newton, and P. G. Sheasby, "On-line

nugget expulsion detection for aluminum spot welding and weldbonding," SAE

Trans. Journal of Materials and Manufacturing, vol. 105, pp. 209-218, 1996.

[65] R. R. Yager and D. P. Filev, Essentials of Fuzzy Modeling & Control New York:

John Wiley & Sons

1994.

[66] W. Li, F. Eugene, and D. Cerjanec, "Energy Consumption in AC and MFDC

Resistance Spot Welding," in Sheet Metal Welding Conference XI Sterling

Heights, MI, May 11-14, 2004.

[67] N. T. Williams and W. Waddell, "The Importance of Electrode Tip Growth When

Welding Zinc Coated Steels," in International Institute of Welding , Doc.III-1032-

94, 1994.

[68] S. W. Howes and J. S. H. Lake, in Sheet metal Welding Conference Detroit, MI,

AWS Detroit section, 1990.

135

[69] R. Holliday, J. Parker, and N. Williams, "Prediction of Electrode Campaign Life

When Spot Welding Zinc Coated Steels Incorporating Electrode Tip Dressing

Operations," Ironmaking & Steelmaking, vol. 23, pp. 157-163, 1996.

[70] D. P. Lu F., "Model for estimating electrode face diameter during resistance spot

welding," Science and Technology of Welding and Joining, vol. 4, pp. 285-289,

1999.

[71] J. Matejec and M. Zelenak, "Resistance Spot Welding of Zinc Coated Steel

Sheets with Electrode Dressing," International Institute of Welding, Doc. no.:III-

1023-93, 1993.

[72] F. J. Ganowski and N. T. Williams, "Advances in Resistance Spot and Seam

Welding of Zinc-Coated Steel Strip," vol. 49, 1972.

[73] R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis,

Fourth ed.: Prentice -Hall, 1998.

[74] W. Li, J. Ni, and S. J. Hu, "On-line Expulsion Detection and Estimation for

Resistance Spot Welding," Technical Paper - Society of Manufacturing

Engineers, 1999.

136

ABSTRACT

DYNAMIC RESISTANCE BASED INTELLIGENT RESISTANCE WELDING

by

MAHMOUD EL-BANNA

MAY 2006

Advisor: Dr. Ratna Babu Chinnam and Dr. Dimitar Filev

Major: Industrial Engineering (Manufacturing)

Degree: Doctor of Philosophy

Resistance spot welding (RSW) is one of the most popular processes employed

for sheet metal assembly. Although used in mass production for several decades, RSW

poses several major problems, most notably, huge variation in weld quality. The

strategy employed by the automobile OEMs to reduce the risk of part failure is to often

require more welds to be performed than would be needed to maintain structural

integrity if each weld was made reliably. Advances over the last decade in the area of

non-intrusive electronic sensors, signal processing algorithms, and computational

intelligence, coupled with drastic reductions in computing and networking hardware

costs, have now made it possible to develop non-intrusive intelligent resistance welding

systems that overcome the above shortcomings.

The research develops an Intelligent Resistance Welding System that improves

the weld quality and reduces the number of welds needed. In particular, there are three

specific research achievements: 1) Development of a resistance welding monitoring

137

system based on Linear Vector Quantization (LVQ) algorithm for accurate in-process

non-destructive classification of nugget quality by using the dynamic resistance (or

voltage) profile, 2) Development of a fuzzy control scheme for adapting the controller

set point for weld quality enhancement, and 3) Development of an algorithm for on-line

evaluation of the electrode condition right after tip dressing.

The fuzzy control scheme developed for adapting the welding controller set point

relies on two soft sensors for expulsion detection as well as weld quality evaluation. The

objective is to operate the welding process just beneath the expulsion level conditions to

achieve optimum weld strength. The adaptive fuzzy control scheme was successful in

reducing the number of bad welds, cold or expulsion welds, when used on Medium

Frequency Direct Current (MFDC) constant current control against the traditional

stepper/no stepper techniques.

Fuzzy C-means clustering algorithm implemented in a hierarchal fashion is used

to evaluate the electrode condition right after tip dressing. The algorithm was

successfully verified on constant current and constant heat alternating current

controllers.

138

AUTOBIOGRAPHICAL STATEMENT

MAHMOUD EL-BANNA

Education

• Currently attending PhD program at Industrial and Manufacturing Engineering, Wayne State University, Detroit, Michigan. (3.8 GPA). (Area of concentration: Intelligent Manufacturing Systems).

• Master of Science in Industrial Engineering (MSIE), University of Jordan, Amman, Jordan. Feb. 2001. Top 5%. (Area of concentration: Manufacturing Processes)

• Bachelor of Science in Mechanical Engineering (BSME), University of Jordan, Amman, Jordan. July 1998 Top 5%. (Area of concentration: Manufacturing and Quality)

Experience Oct/2004 – Present: Project Engineer at Ford Motor Company Wayne State University, Detroit, MI, USA. Project: Intelligent Resistance Welding. Feb/2002 – Oct/2004: Graduate Research Assistant

Wayne State University, Detroit, MI, USA. Projects: Hot Metal Gas Forming, Hydro Forming (HMGF). Magnesium Manufacturability. Carbon Nanotube. June/98 – Dec/2001: Manufacturing Engineer

Agriculture Plastic Company, Amman, Jordan. June/98 – Feb/2001: Graduate Teaching Assistant

University of Jordan, Amman, Jordan. Research Interests

• Medical devices and Equipment: (micro manufacturing, especially intelligent micro-welding).

• Health Care Systems: (intelligent health care systems; multi-agent based systems)

• Nanotechnology: (in particular application of carbon Nanotube in metal matrix) • Extrusion: diagnostics and prognostics for extrusion processes.

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