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
ii
DEDICATION
To my family; father, mother, brothers, sister and for the first baby in my family, my
nephew “Yanal”
iii
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
vi
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
dynamic resistance profile with the neural network ................................................ 47
Table 9, Type 1 and 2 errors for expulsion welds classification when using the entire
dynamic resistance profile with the neural network ................................................ 47
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
dynamic resistance profile with the neural network ................................................ 49
Table 12, Type1 and 2 errors for normal welds classification when using maximum of
dynamic resistance profile with the neural network ................................................ 49
Table 13, Type1 and 2 errors for expulsion welds classification when using maximum of
dynamic resistance profile with the neural network ................................................ 49
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
dynamic resistance profile with the neural network for AC controller ..................... 51
Table 16, Type1 and 2 errors for expulsion welds classification when using the entire
dynamic resistance profile with the neural network for AC controller ..................... 52
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
dynamic resistance profile with the neural network for AC controller ..................... 53
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
ix
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
x
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
xi
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 30, Schematic for set up test .............................................................................. 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 38, Schematic for set up test .............................................................................. 94
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
2
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
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 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
2.00 mm gage, HSLA, hot dip galvanized
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)
Element 0.85 mm gage, HSLA, electrogalvanized
2.00 mm gage, HSLA, hot dip galvanized
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
Decision Ho is true Ho is false
Reject Ho α =0.45 1-α =0.55
Don’t reject Ho 1- β =0.46 β =0.54
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
Don’t reject Ho 1- β =0.69 β =0.31
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
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 =0.88 β =0.12
Table 12, Type1 and 2 errors for normal welds classification when using maximum of dynamic resistance profile with the neural network
Ho: Weld is
Normal True State of Null Hypothesis
Statistical
Decision Ho is true Ho is false
Reject Ho α =0.29 1-α =0.71
Don’t reject Ho 1- β =0.81 β =0.19
Table 13, Type1 and 2 errors for expulsion welds classification when using maximum of dynamic resistance profile with the neural network
Ho: Weld is Expulsion True State of Null Hypothesis
Statistical Decision Ho is true Ho is false
Reject Ho α =0.23 1-α =0.77
Don’t reject Ho 1- β =0.87 β =0.13
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
Ho: Weld is Cold True State of Null Hypothesis
Statistical
Decision Ho is true Ho is false
Reject Ho α =NA 1-α =NA
Don’t reject Ho 1- β =0.97 β =0.03
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
Normal True State of Null Hypothesis
Statistical
Decision Ho is true Ho is false
Reject Ho α =0.05 1-α =0.95
Don’t reject Ho 1- β =0.64 β =0.36
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
Ho: Weld is Expulsion True State of Null Hypothesis
Statistical Decision Ho is true Ho is false
Reject Ho α =0.37 1-α =0.63
Don’t reject Ho 1- β =0.98 β =0.01
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
Ho: Weld is Cold True State of Null Hypothesis
Statistical
Decision Ho is true Ho is false
Reject Ho α =NA 1-α =NA
Don’t reject Ho 1- β =1.00 β =0.00
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
Normal True State of Null Hypothesis
Statistical
Decision Ho is true Ho is false
54
Reject Ho α =0.04 1-α =0.96
Don’t reject Ho 1- β =0.75 β =0.25
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
Ho: Weld is Expulsion True State of Null Hypothesis
Statistical Decision Ho is true Ho is false
Reject Ho α =0.25 1-α =0.75
Don’t reject Ho 1- β =0.96 β =0.04
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]
Figure 30, Schematic for set up test
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.
Table 21, 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
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
Table 22, Element analysis for the base tested materials (weight percent)
Element 0.85 mm gage, HSLA, electrogalvanized
2.00 mm gage, HSLA, hot dip galvanized
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
Figure 38, Schematic for set up test
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.
Table 27, 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 28, Element analysis for the base tested materials (weight percent)
Element 0.85 mm gage, HSLA, electrogalvanized
2.00 mm gage, HSLA, hot dip galvanized
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)
Element 0.85 mm gage, HSLA, electrogalvanized
2.00 mm gage, HSLA, hot dip galvanized
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
Figure (47), shows the result of the fuzzy C-mean clustering algorithm for the
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
Weld after tip dressing in the last cluster
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).
Figure (49), shows the result of the fuzzy C-mean clustering algorithm for the
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
Table 33, shows the number of clusters and their sizes from the training mode,
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
Weld after tip dressing in the last cluster
Figure 46, Number of clusters obtained from training mode (4 clusters) for CCC.
Figure (50), shows the result of the fuzzy C-mean clustering algorithm for the
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
In conclusion, all of the welds performed after the electrode tip dressing were
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
Weld after tip dressing in the last cluster
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).
Figure (52), shows the result of the fuzzy C-mean clustering algorithm for the
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
Weld after tip dressing in the last cluster
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
Table 35, 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
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
Figure (53), shows the result of the fuzzy C-mean clustering algorithm for the
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).
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 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
Weld after tip dressing in the last cluster
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).
Figure (55), shows the result of the fuzzy C-mean clustering algorithm for the
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
Table 37, shows the number of clusters and their sizes from the training mode,
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
Figure (56), shows the result of the fuzzy C-mean clustering algorithm for the
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).
In conclusion, all of the welds performed after the electrode tip dressing were
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
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) 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
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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.