IN-PROCESS SURFACE ROUGHNESS RECOGNITION (IPSRR) SYSTEM USING ACOUSTIC EMISSION (AE), PART II
NGU SIONG HO
This project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Engineering with Honours
(Mechanical Engineering and Manufacturing System)
Faculty of Engineering UNIVERSITI MALAYSIA SARAWAK
2004
Dedicated to my beloved family and love one
11
ACKNOWLEDGEMENT
The author wishes to convey his heartfelt thanks and gratitude to Mr. Abdullah bin Haji
Yassin for his supervision, expert advice and tremendous assistance in conducting this
thesis. The author thanks his for the times spend in helping to expand and correct the ideas
about the thesis, enable the author to learn a lot of new things and enhance his skills in
analyzing the data. Without his immeasurable guidance, skill and patience, the author would
not be able to enhance and improve the contents of this thesis to as it is now.
With this opportunity, the author also wishes his profound gratitude and appreciation to
all the participating individuals especially Associate Professor Dr. Sinin Hamdan and Mr.
Stanley Leo for their tremendous assistant and advice throughout the project.
The author would also like to thanks the staffs of the Faculty of Engineering, Universiti
Malaysia Sarawak and all the individuals who have help the author in the process of
conducting and computing the thesis.
Finally, the author would like to express his gratitude to his loving family for their
endless support and encouragement in finishing this thesis.
iii
ABSTRACT
Ramp-up is a most important step in the implementation of manufacturing systems, and is
even more significant in reconfigurable manufacturing systems. For a successful reduction in
ramp-up time, it is essential to analyze and monitor both the overall manufacturing system
and the individual machine tool or processes that comprise the system. Towards the whole of
the project, we have to deal with the issue of monitoring surface roughness using acoustic
emission to enable faulty conditions related with surface roughness to be recognized during
the process before the quality of end product out of specification. Acoustic emission
generated from the cutting process is analyzed as a source for monitoring surface roughness
during turning. In engineering applications, surface roughness measurements barely done
during machining process, have been done barely. In this study, computer linked with
acoustic emission devices were carried out to measure surface roughness during turning
process. Throughout the project, results shown that the spindle speed are key parameter that
takes effect on the acoustic emission generation. However, this only applies in lower spindle
speed in conventional central lathe machine.
iv
ABSTRAK
Peningkatan produktiviti merupakan langkah terpenting dalam perkenalan sistem
pembuatan, dan lebih bermakna dalam penyusunan semula sistem pembuatan. Bagi
pengurangan dalam masa peningkatan produktiviti, ia adalah keperluan untuk menganalisis
dan memerhati keseluruhan system pembuatan dan peralatan mesin atau proses-proses yang
terdapat dalam sistem. Keseluruhannya, kita mengatasinya dengan isu pemerhatian
kekasaran permukaan melalui penggunaan `acoustic emission' supaya dapat membuat
pengesanan awal mengenai kecacatan permukaan semasa proses sebelum qualiti benda kerja
diluar spesifikasi. `Acoustic emission' dihasilkan melalui proses pemotongan dianalisis
sebagai punca untuk pemerhatian kekasaran permukaan semasa proses. Dalam penggunaan
kejuruteraan, ukuran kekasaran permukaan jarang dilakukan semasa proses memesin. Dalam
kajian tersebut, peralatan `acoustic emission' disambung kepada computer untuk mengukur
kekasaran permukaan semasa proses pemotongan. Keseluruhannya, keputusan menunjukkan
kelajuan spidal merupakan parameter utama yang menyebabkan penghasilan `acoustic
emission' tertentu. Akan tetapi, ini hanya diaplikasikan dengan kelajuan spindal yang rendah
dalam mesin larik konventional.
V
TABLE OF CONTENTS
THESIS TITLE
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
ABSTRAK
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
NOMENCLATURE
CHAPTER I INTRODUCTION
1.1 BACKGROUND
1.2 PRINCIPLES OF ACOUSTIC EMISSION
1.3 OBJECTIVE
CHAPTER 2 LITERATURE REVIEW
2.1 SURFACE ROUGHNESS
2.1.1 Interpreting Surface Roughness
2.1.2 Surface Roughness Measurement
2.1.2.1 Visual comparison
2.1.2.2 Tactual comparison
2.1.2.3 Direct-reading stylus type
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V
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I
2
3
4
5
6
6
6
8
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2.1.3 Analyze Surface Roughness
2.2 ACOUSTIC EMISSION
2.2.1 Generating Mechanisms
2.2.1.1 Types of mechanisms
2.2.1.2 Real materials
2.2.1.3 Burst and continuous emission
2.2.2 Acoustic Waves
2.2.2.1 Type of acoustic waves [2]
2.2.2.2 Attenuation [2]
2.2.3 Sensors
2.2.3.1 Piezoelectric
2.2.3.2 Size effects
2.2.3.3 Couplants
2.2.3.4 Sensor sensitivity [2]
2.2.4 AE Signals
2.2.4.1 Signal characteristic
2.2.4.2 Frequency spectra
2.2.4.3 Signal energy
CHAPTER3 METHODOLOGY
3.1 INTRODUCTION
3.2 SPECIMEN'S MATERIAL
3.3 EXPERIMENT PROCEDURE
3.4 MACHINING EQUIPMENT
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12
12
13
14
16
17
19
20
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23
24
25
25
26
26
27
28
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30
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3.4.1 AJAX AJ165SS Central Lathe Machine
3.4.2 Tungsten Carbide Cutting-tools
3.5 ANALYSIS EQUIPMENT
3.5.1 Acoustic Emission Analysis Devices
30
32
33
33
CHAPTER 4 RESULTS AND DICUSSION
4.1 INTRODUCTION 38
4.2 ACOUSTIC EMISSION ANALYSIS 38
4.2.1 Acoustic Emission And Depth Of Cut 38
4.2.2 Acoustic Emission And Feed Rate 40
4.2.3 Acoustic Emission And Spindle Speed 42
4.3 CORRELATION BETWEEN ACOUSTIC EMISSION AND 47
ARITHMETIC AVERAGE ROUGHNESS
4.4 DICUSSION 49
4.4.1 Acoustic Emission Analysis 51
4.4.2 Correlation Between Acoustic Emission And Arithmetic Average 52
Roughness
CHAPTER 5 CONCLUSION AND RECOMMENDATIONS
5.1 CONCLUSION
5.2 RECOMMENDATIONS
54
55
References 57
Appendices 60
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LIST OF FIGURES
CHAPTER 2 LITERATURE REVIEW
Figure 2.1 Surface finishes available by common production methods [5] 5
Figure 2.2 Symbols used to designate surface finish [3] 6
Figure 2.3 Surf-check roughness standards (tactual and visual comparison) [3] 8
Figure 2.4 Direct-reading stylus type [3] 9
Figure 2.5 Schematic of a surface profile z(x) [4] 11
Figure 2.6 Burst emission mixed with continuous emission [2] 15
Figure 2.7 Particle displacement for bulk acoustic plane waves frozen at an 18
instant of time. (a) Longitudinal waves and (b) Transverse waves [2]
Figure 2.8 Particle displacements for acoustic waves frozen in time. 19
(a) Rayleigh waves; (b) Plane wave, first symmetric mode;
(c) Plane wave, first antisymmetric mode [2]
CHAPTER 3 METHODOLOGY
Figure 3.1 AJAXAJ165SS central lathe machine
Figure 3.2 Tungsten carbide insert with tool holder
Figure 3.3 MICRO 30 (S) EZ 90 sensor
Figure 3.4 PAC's 1220A preamplifier
Figure 3.5 Wide bandwidth AE amplifier
Figure 3.6 PICO ADC-200
Figure 3.7 PicoLog software on computer screen
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32
34
35
35
36
37
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CHAPTER 4 RESULTS AND DICUSSION
Figure 4.1 Acoustic Emission maximum and minimum value, dB (refer Appendix E) 39
Figure 4.2 Graph acoustic emission against feed rate with depth of cut, 0.2 mm 40
(refer Appendix F)
Figure 4.3 Graph acoustic emission against feed rate with depth of cut, 0.4 mm 41
(refer Appendix F)
Figure 4.4 Graph arithmetic average of acoustic emission versus spindle speed 43
(0.2 mm depth of cut)
Figure 4.5 Graph arithmetic average of acoustic emission versus spindle speed 44
(0.4 mm depth of cut)
Figure 4.6 Standard deviation of acoustic emission versus spindle speed 46
(0.2 mm depth of cut)
Figure 4.7 Standard deviation of acoustic emission versus spindle speed 46
(0.4 mm depth of cut)
Figure 4.8 Graph AE and Ra against spindle speed 48
(0.4 mm depth of cut and 0.05 mm/rev. feed rate)
Figure 4.9 Graph AE and Ra against spindle speed 48
(0.4 mm depth of cut and 0.15 mm/rev. feed rate)
Figure 4.10 Graph acoustic emissions against spindle speed 50
(0.4 mm depth of cut with 0.05 mm/rev. and 0.10 mm/rev. feed rate)
Figure 4.11 Graph correlation between AE and Ra 52
Figure 4.12 Graph correlation between AE and Ra 52
X
LIST OF TABLES
CHAPTER 3 METHODOLOGY
Table 3.1 Experiment parameters involved (refer Appendix C)
Table 3.2 PicoLog software setup for experiment
Table 3.3 The partial specification of AJ165SS central lathe machine
Table 3.4 The specifications of MICRO 30 (S) EZ 90 sensor
Table 3.5 The specifications of PAC's 1220A preamplifier
Table 3.6 The specifications of PICO ADC-200
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30
31
33
34
36
CHAPTER 4 RESULTS AND DICUSSION
Table 4.1 Acoustic emission magnitude (0.2 mm depth of cut) 42
Table 4.2 Acoustic emission magnitude (0.4 mm depth of cut) 42
Table 4.3 Standard deviation of acoustic emission (0.2 mm depth of cut) 45
Table 4.4 Standard deviation of acoustic emission (0.4 mm depth of cut) 45
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NOMENCLATURE
Surface (refer Appendix A)
Surface irregularity This term applies to the deviation of the actual surface from a nominal
surface. It includes such characteristics as roughness and waviness [3].
Profile The profile of a surface is the contour of any specified cross section of
the surface. The profile discloses waviness, roughness, and
combination of the two. Flaws are also frequently disclosed [3].
Flaws Flaws are unintentional, unexpected, and unwanted interruptions in
the texture [4].
Roughness It is formed by fluctuations in the surface of short wavelengths,
characterized by hills (asperities - local maxima) and valleys (local
minima) of varying amplitudes and spacing [4].
Waviness Waviness is the surface irregularity of longer wavelength and is
referred to as macroroughness [4].
Lay Lay is the principal direction of the predominant surface pattern,
ordinarily determined by the production method [4].
Asperities Asperities are referred to as peaks in a profile (two dimensions) and
summits in a surface map (three dimensions) [4].
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Acoustic Emission (refer Appendix A) 151
AE The class of phenomena whereby transient elastic waves are generated
by rapid release of energy from localized sources within a material, or
the transient waves so generated. Other terms used in AE literature
include stress wave emission, microseismic activity, and emission or
AE with other qualifying modifiers.
AE amplitude Peak amplitude of AE signal during signal duration.
RMSAE Rectified time-averaged AE signal, measured on linear scale and
reported in volts (root mean square voltage).
AE signal Electrical signal obtained by detection of one or more acoustic
emission events.
AE signal duration Time between AE signal start and AE signal end. AE signal start is
the beginning of an AE signal recognized by the system processor,
and AE signal end is the last signal crossing the threshold above the
threshold.
AE, burst Individual emission event generated in brittle materials such as wood,
concrete, and rock.
AE, continuous Repeated emission commonly observed in metal. If the duration of
each event is too short to identify, the event is recognized as a
continuous emission.
Source location Includes zone location, computed location, and continuous location.
Zone location determines the general region of an AE source.
xiii
Computed location is based on algorithmic analysis of difference in
arrival times among sensors. Continuous location is based on
continuous AE signals, as opposed to hit or difference in arrival time
location method. It is commonly used in leak location of pressurized
materials because of the presence of continuous emissions.
Threshold, voltage Voltage level on an electronic comparator such that signals with
amplitudes larger than this level will be recognized. The voltage
threshold may be user adjustable, fixed, or automatic floating (ASTM
E750).
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CHAPTER 1
INTRODUCTION
1.1 BACKGROUND
Many years back, acoustic emission (AE) is a relatively new technique. It started in
1960's when acoustic emission was developed to be used as a Non-Destructive Test (NDT)
method. Acoustic emission technology has been used for many years for the testing of
pressure vessels, piping and other man made structures. Acoustic emission could lead to a
huge number of new measurement techniques is due to convenience and sensitivity.
Normally, acoustic emission is used to detect fracture, creep effect, crack propagation
speed, failure mechanisms in certain materials. These are performed on static equipment.
After many years, applications of acoustic emission were expanded widely to in-process
monitoring system on automated manufacturing. An automated manufacturing operates
continuously without operator intervention. Acoustic emission used in-process monitoring
system and hence introduces a significant tool condition monitoring (TCM) and monitoring
internal activities of material structures. Besides that, the acoustic emission root-mean-
square (RMSAE) energy is also studied to detect tool wear and changes in structures such as
mention in International Journal of Machine Tools & Manufacture, 14 June 1999 and
Acoustics Research Letters Online, 14 November 2002.
I
In automated manufacturing field, surface finish of the product give a first notion to the
process personnel. In-process surface roughness recognition system, surface roughness could
provide sufficient data on tool wear, unfavorable chip shape, lack of coolant. This is able to
prevent or prepare automated manufacturing for the worst.
1.2 PRINCIPLES OF ACOUSTIC EMISSION
Acoustic emission is an elastic wave due to a violent release of energy accumulated in
the material by the propagating microdamage (increase of microgaps, movement of
dislocating groups) in the material [1]. It is generated by the material itself which means that
it does not need any external source introduce to the material to produce it. A simple
example of acoustic emission is the sound of a pencil being broken or wood being split.
Acoustic emission generation mechanism could not be control over but can only subject a
material to conditions which will cause the production sound wave [2].
The link between the source and the acoustic emission signal produced by the sensor is
the acoustic wave [2]. Acoustic emission could be explained as a release of elastic energy
into acoustic wave by the formation of a crack in a solid. Acoustic wave could presents in
any material which includes liquid and gas. The amount of acoustic wave attenuation
depends on the properties of the material. Attenuation is greater in porous materials (such as
wood) and viscoelastic materials than in metallic materials.
2
Acoustic emission is used to nondestructively monitor structural integrity and
characterize the behavior of materials when they undergo deformation, fracture, or both. It is
different from ultrasonic or radiographic techniques, acoustic emission does not require
external energy; acoustic emission is released from the test piece itself. Acoustic emission
might not the only NDT method that can be used to monitor defects during manufacturing
since there are other methods available in nowadays such as optical method, scanning probe
microscopy (SPM) method, electron microscopy method and others. Other conventional
NDT methods require that the line be interrupted to test the materials. Acoustic emission
techniques have been used for monitoring components and systems during manufacturing,
detecting and locating leaks, mechanical property testing, and testing pressurized vessels.
1.3 OBJECTIVE
The objectives of the project are:
Using acoustic emission tool kit comprising sensors, instrumentation and software
for in-process machining monitoring to obtain measurement on surface roughness.
Surface roughness data comparison between direct-reading stylus (Mitutoyo Surftest
SJ-400) method and Acoustic Emission method.
To explore acoustic emission processing techniques that can correlate surface
roughness and acoustic emission signals. As well as surface finish prediction by
using acoustic emission.
3
CHAPTER 2
LITERATURE REVIEW
2.1 SURFACE ROUGHNESS
In manufacturing world, surface finish of the product is a very important issue to the
process engineer. This is because it relates in product function, appearance and economy of
manufacturing. It determines which surfaces must be re-machined to some degree of
workpiece quality acceptance. Explanation of the quality, manufacturing technology and
material characteristic of the workpiece roughly become ease when explore the surface
finishing of manufacturing.
Surface roughness depends on the type of cutting operation. Different kind of cutting
operation leave behind different range of surface roughness on the product surface (refer
Figure 2.1). It is because in cutting process, it is related to the cutting action of the tool,
abrasives or other finishing devices used which include condition of the tool and type of
material being worked. It is, therefore create six surface characteristics which are lay
parallel, perpendicular, angular to the boundary line of the surface, multidirectional,
approximately circular relative to the center of the surface and approximately radial relative
to the surface.
4
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Figure 2.1 Surface finishes available by common production methods [5].
2.1.1 Interpreting Surface Roughness
In turning cutting process, there is only two terms most familiar which are waviness and
lay. Waviness is irregularity of the normal surface occurring at greater spacing than
roughness while the lay is the direction of the predominant surface roughness [3]. Since it is
concern with turning cutting process, the surface roughness of the workpiece will not
considering any flaws in it. For that, only waviness and lay should appear on the surface of
5
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the workpiece which results from the operating factors such as machine or work deflection,
vibration, heat treatment, tool marks. This is determined by the production method used.
It is necessary to interpret surface roughness before attempting to measure roughness
because surface roughness measurements are influenced by the direction in which cutting
operation taken across the surface. Surface roughness generally measured across the lay
since the highest readings can be recorded is in this direction. All the information regarding
the surface roughness which achieve from the measurement could be interpreted into symbol
system as shown in Figure 2.2.
Maximum Waviness height
Roughness Height, (Arithmetic Average)
Maximum t:
-.,,.. _ : ý°l - Roughncas -Wtdth CutöSi
ÄZýIlýlk111ID ýýý 3ý ,1 _- _ -----äR2srimumýRnuni%n&ee
{Ufdfh ,-.
Y", ".... ý v~ý i "ý.... ýn aww
:"J. _ý" Uli ---; -
.v__.
,t-,,
Figure 2.2 Symbols used to designate surface finish [3].
2.1.2 Surface Roughness Measurement
Surface roughness measurement can be conduct in three conventional ways basically
which are visual comparison, tactual comparison and direct-reading stylus-type instrument.
.. _. .ýý,., _., ý.,. _ýýý
... '
.t.... ,..
». -. ,. t ýýý .. "
't` ,' Maximüm ; Wavtness Width
6
These methods widely use until now because it concern with the cost to implement,
complexity of the system and accuracy of the result.
2.1.2.1 Visual comparison
Visual comparison is the most basic way to categorize the quality of certain workpiece. It
is come to in use which compares the workpiece with the available piece. This method has
been upgraded to a level where some standard of comparison has been included in the
measurement system.
This is the simplest way to do measurement but it has some disadvantages such as
comparisons using the unaided eye are not considered as accurate as other methods because
of differences in color, reflection characteristics, and differences in the materials being
compared [3]. There is only one way to improve this method is by implementing certain
equipment such as stereoscopic comparison microscope which improve the satisfactory
results that have been obtained.
2.1.2.2 Tactual comparison
Tactual comparison is a method using touching sense to compare the workpiece with the
unique piece. Tactual comparison is so commonly use because it can solve the cost problem
and provide a rapid method of making surface comparison. Roughness standards have also
7
been developed by using this method. Such standard might made from thermosetting or steel
type and it is specially manufacture for tactual comparison and visual comparison purpose.
The disadvantage of the tactual comparison is the human fingernail would not be able to
differentiate in measurement in millionths of an inch with that sensitivity.
Figure 2.3 Surf check roughness standards (tactual and visual comparison) [3].
2.1.2.3 Direct-reading stylus type
Direct-reading stylus type is a system equipped with a stylus pickup to gather the
information regarding the surface roughness from the specimen. Movement of the stylus
pickup is across the surface especially the lay since it is the highest reading in this direction.
The reading could be shown in meter type or recorded in paper tape.
Meter type is more convenient when consider using it in narrow space, sites and different
places. The paper tape need a certain space for functioning but it is reliable than the meter
type because less subject to human error. The tape contains permanent record about the
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surface profile after being record. Surface roughness in uniformly rough or uniformly
smooth after being machined. Only paper tape roughness reading could fully evaluated in
duplex surfaces result when a secondary operation is performed on the surface to remove the
high spot.
Figure 2.4 Direct-reading stylus type [3].
Several characteristics can be identified by the direct-reading stylus-type instrument
which is roughness height, roughness width, and waviness height and width. All the
information could be used to categorizing the characteristics of the surface roughness.
2.1.3 Analyze Surface Roughness
Surface roughness related closely to roughness height. In order to determine surface
roughness, there are two methods being used to calculate roughness height, which is the
arithmetic average, (R,, ) and the root mean square average (RMS) [3]. Both methods are
related and the root mean square average is approximately 10% larger than the arithmetic
average.
9
Roughness height calculation starts with a mean line is drawn through the recorded
surface profile in such a way that the areas above mean line approximately equal with the
below the mean line (refer Figure 2.5). Organized measurements are then taken from the
mean line to the profile and recorded. The arithmetic average height of the profile is
expressed as follows [4]:
R°-ýfo I`-mýdx .........................
(2.1)
Where, L= sampling length of the profile
z= profile
in = value of mean line from the reference line
x= length of sample measurement point from
the end of specimen
=0 (if mean line is on the reference line)
m=ý ýLzdx
The root mean square average height of the profile is expressed as follows [4]:
CT` = Jo (z-rn)dx
=R4 2-rn=
R2= RMSýý2
=1 fa (Z2 )Q'JC
FMSac =4 ý4 ý
......................... (2.2)
10
