detection of laser-welding defects using neural...
TRANSCRIPT
Detection of laser-welding defects using
neural networks
BY Marc Auger
A thesis submitted to the Department of Mechanical
Engineering in confomiity with the requirements for the
Degree of Master of Science (Engineering)
Queen's University
Kingston. Ontario. Canada
September. 200 i
Copyright O Marc Auger, 2 0 1
National Libraiy 191 .canada Bibliothèque nationaie du Canada
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To Tiffany
Laser welding is becoming more and more important in the automotive industry
and qudity of the weld is critical for a successful application. In many cases, the increase
in welding speed provided by laser welding has caused the welding system operator to be
unable to keep up with the production rate while fully inspecting each part. Therefore,
either additional inspecton are required or some fom of real-time on-line inspection of
the weld must be provided. This is especidly necessary where the laser weld propenies
are critical to the final performance.
This thesis describes a system for the prediction of various panmeters of the
fusion zone of a weld from the emitted radiation during laser welding. A neural network
system is used to associate data from three photodiode senson to geometrical properties
of the fusion zone rneasured in cross-section.
A machine welding automotive transmission gears with a CO2 laser was used to
test the system. The neural network system was able to predict. with acceptable accuracy,
two of the most important parameten describing the geometry of the fusion zone: the
total area and the lateral position of the fusion zone relative to the weld seam. The system
shows promise in king able to predict unacceptable welds if incorporated as part of an
on-Iine quality monitoring process.
Acknowledgements
The author wishes to express his sincerest gratitude to his thesis supervisor for
their support and guidance throughout this research. Prof. P.M. Wild and Prof. A.
Ghasempoor. This work would not have been possible without the assistance of ATC
Powerlasers of Kitchener, Ontario who donated time and technical suppon on the
experimental appmus used for dl experiments. Special thanks goes out to Rob Mueller
and H o n ~ i n g Gu for their advice. rxperience and knowledge with regards to laser
welding. The assistance provided by George Pinho in performing al1 of the tests was
gatefully appreciated and duly noted. A special thanks is also given to Jack Evanecky of'
DaimleiChrysIer. Kokomo. Indiana for donating al1 the material used in the experi ments
and the use of his equipment. The shll and talent of Chris Howes and Charlie Cooney in
the Metallurgy lab were indispensable during the Iaboratory analysis. This work would
not have k e n possible without the financial support of the Centre for Automotive
Materials and Manufactunng, Kingston. Ontario. Finally, 1 would like to thank my wife
for her suppon and understanding throughout the duration of this work.
... AbStract ...................... ~............W............................................................................ ................... UI
Acknow ledgem~nîs ........... .. ....... .................... ...... ...................... iv
.............. Table of Contents ... ......... ............................... .... ......................................................................... v
List of Tables .. ............. ..... .... ........o.........mw........................................................................................... ix
.. .o.. List of Figures ............................................ "...*...w....m....-..m.w........w................................................... x
Nomenclature ................... ..................... ........ .. ..... .. ........ ............. ....... ... ............................................... xiv
Chapter 1 Introduction .............. H ......- ~ e . . . ~ . . e w ~ . * H H . . H H . . . . e o . . . 1
Background ...................................................................................................................................... 1
k r Welding in the Automotive Industry .................................................................................... 2
................................................................. Examples of Laser Welding in the Automotive Industry 3
................................................. Quality C o n m l in the Manufacture of Laser Welded Components 6
........................................................................................................................................ Objective 10
Outline of Thesis ............................................................................................................................ I I
Chapter 2 Literature Review ..................... ........... ............ .. ............... 12
2.1 Introduction .................................................................................................................................... 13
2.2 k r Welding Process ................................................................................................................... 13
2.3 k r Classification ...................................................................................................................... 14
4 Laser Weld Monitoring .................................................................................................................. 16
2.5 Pre-Process Monitoring .................................................................................................................. 17
2.6 Post-Rocess Monitoring ................................................................................................................ 19
................................................................................................................... 2.6.1 Opticai Methods 19
7.6.2 Ultrasonic .......................................................................................................................... 10
......................................................................................................... 2.6.3 Uluasonic & Magnetic 21
............................................................................................................................... 2.6.1 Magnetic 21
.................................................................................................................... 2.7 In-Rocess Monitoring 22
................................................................................................................................ 2.7.2 Acoustic 25
.......................................................................................................... 2.7.3 Acoustic and Cameras 26
2.7.4 Optical Emissions in Laser Welding ..................................................................................... 26
2.7.5 Photodiodes .......................................................................................................................... 27
2.7.5.1 Placement of Photodiodes ................................................................................................ 28
2.7.5.2 Infrared Photodiodes ........................................................................................................ 29
....................................................................................................... 2.7.5.3 Visible Photodiodes 30
............................................................................................................... 2.7.5.4 UV Photodiodes 30
...................................................................... 2.7.6 Photodiodes and Optical or Acoustic Senson 31
........................................................................................................................ 2.7.7 Other Sensors 33
........................................................................................................................... 2.8 Signal Rocessing 34
................................................................................................................ 2.8.1 Swtistical Methods 34
2.8.2 FuuyLogic ........................................................................................................................ 35
2.8.3 NeuralNetworks ................................................................................................................. 36
2.8.4 Combinations ........................................................................................................................ 38
2.9 Conclusions .......~............................................................................................................................ 39
.................................................................................................................................... 3.1 Introduction 41
3.2 Multilayer Feedforward Neural Networlcs ..................................................................................... 42
................................................................................................................................ 3.1.1 Training 45
............................................................................................................................. 3 2.2 Validation 48
...................................................................................................... 3.3 Categories of Neural Networks 50
........................................................................................ 3.4 Relating Weld Geometry to Signal Data 51
Chapter 4 Experimcnlsl Pmecdure .................................................................................................... 54
4.1 Introduction ................................................................................................................................... 54
4.2 Laser Welding System .................................................................................................................. 54
42.1 Monitoring System .............................................................................................................. 58
4.3 Experimental Parameters ................................................................................................................ 61
4.4 Sample Preparation ........................................................................................................................ 65
..................................................................................................................... 4.4 . 1 Image Analysis 67
4.5 Dau Preparation ............................................................................................................................. 73
4.5.1 Spectnim Analysis .............................................................................................................. 75
Chapter 5 Experirnentd Resuîts .................. ..........1........................................ ....................... 77
.................................................................................................................................... 5.1 Introduction 77
5.2 Geornevical Proprties of the Fusion Zone ................................................................................... 77
5.2.1 Area of the Fusion Zone ....................................................................................................... 78
........................................................................................................... 5.2.2 Thickness of the Disk 78
5.2.3 AraoftheHoles .................................................................................................................. 78
......................................................................................................... 5.3 Neural Network Architecture 79
Evaluating Performance of the Neural Mode1 ...................................................................... 79
Training using Sçquential Data ........................................................................................... 81
....................................................................................... Training by Randomizing the Data 83
.............................................................................................................. Input Normalization 85
Two Hidden hyer Neural Network ..................................................................................... 87
Elimination of Samples Containing Porosity ....................................................................... 89
Improving Generalization by Adding bise ......................................................................... 90
Optimizing Network Training .............................................................................................. 93
5.4 Summ ary ..... .....................*............ .. ........................*......... 99
vii
................ Cbapter 6 CoacIusioa ....... ..... .................... .............................................................. 10()
6.1 Contributions ................................................................................................................................ 100
6.2 Concluding Remarks .................................................................................................................... 100
6.3 Recommendations ........................................................................................................................ 103
..... References ................................................... ...... ............................................................ .............. 105
.......... Appendix A ........................ ................. ................................................................ ......... 115
Appendix B ............................................................................................................................................ 116
............................................................................................................. Appendix C .......................... ..... 117
Vita ...*............... .... ................................................ ......... ..... ... ........................................ 119
List of Tables
Table 3.1 . Example of limits used for selected spectrum analysis coefficients .............. 52
Table 1.1 . Typical parameten in a rotary laser welder ................................................... 5 8
List of Figures
7 ....................................................... Figure 1.1 . Example of single-sided laser welding [3]
..................... Figure 1.2 . Tailor welded blank and siamped part of a dwr inner panel [5] 4
Figure 1.3 . Roof panel laser welding at Volvo [6] ........................................................... 5
...................................................... Figure 1.4 . Hemisphencal simulative fonning test [7] 7
Figure 1.5 . Auto/Steel Partnership concavity specifications [IO] ..................................... 7
..................................... Figure 1.6 . AutolSteel Partnership convexity specifications [IO] 8
........................................ Figure 1.7 . Section of ISO 139 19- 1 describing weld quality(91 8
Figure 1.8 Absolute values (a) and tolerance limits around a baseline signal (b) as
............................................................... statistical techniques for signai processing 10
.................................. Figure 2.1 . Schematic of conduction (a) and keyhole (b) welding 13
Figure 1.2 -Cross section of conduction (a) and keyhole (b) full penetration welds [17] 13
Figure 2.3 . Laser types and their charactenstic parameten [L 81 ..................................... 14
................. . Figure 2.4 Characteristic bearn profiles of CO2 (a) and Nd:YAG (b) lasen 15
Figure 2.5 . Follower wheel for component welding [20] ................................................ 17 0 Figure 2.6 . SOUDRONIC edge preparation system [23] ........................................... 18
Figure 2.7 . Laser triangulation principle for profile acquisition [24] .............................. 20
...................................................................... Figure 2.8 . EMAT defect detection [28] 2 1
Figure 2.9 . Schematic of Magnetic Flux Leakage apparatus 1321 ................................... 22
Figure 2.10 . Two methods to mount in-process senson .................................................. 23
Figure 2.11 . View of the molten pool surface; a) Sketch of the geometrical properties, b)
Non-disturbed video image . c) Video image of part misalignment . (361 ................. 24
.............................................. Figure 2.12 . Different CO-axial mounting techniques [12] 29
Figure 2.13 . Extemal mounting of photodiodes and cameras in a single quality
monitoring system [37] .................................... .... ..................................................... 32
Figure 2.14 . Rinciple of potentid in the weld plume [62] ............................................ 33
Figure 2.15 . Exarnple of a membership function that allows an uncertain answer to a
Boolean equation ....................................................................................................... 36
Figure 2.16 . Mode1 of a single neuron [72] ..................................................................... 37
Figure 3.1 . Typical multilayer feedforward neural network architecture [78] ................ 42
Figure 3.2 . Neuron wi th multiple inputs and a single output [78] ................................... 43
Figure 3.3 . A sigrnoid function [78] ............................................................................... 44
Figure 3.4 - Gradient descent on a 2-D contour plot of an error function ........................ 46
Figure 3.5 - Oscillations with gradient descent on a 2-D contour plot of an error function
.................................................................................................................................. 46
Figure 3.6 . Gradient descent on a 2-D contour plot of m error function with a
momentum term ........................................................................................................ 47
Figure 3.7 . Typical cross section of network error function [79] ................................... 48
Figure 3.8 -Training iuid test error as a function of training iterations ............................. 49
Figure 3.9 . Pynmid rule for selecting number of hidden neurons in a three-layer
network [79] ............................................................................................................. 52
Figure 4.1 . Laser welding cell by ATC Powerlasers for DaimlerChrysler ...................... 55
Figure 4.2 . Gear components: shaft & cup (a), welded subassembly (b) ........................ 56
Figure 4.3 . Weld sequence on an un-weided plate ......................................................... 56
........................................................................... . Figure 4.4 Shield gas nozzle location 57
Figure 1.5 - Responses curve of the three photodiodes of the ATC WPM system [82] .. 59
Figure 4.6 - Weld Process Monitor from ATC Powerlasers [82] ..................................... 60
Figure 4.7 - Close-up of the WPM display screen [82] .................................................... 61
................................................... Figure 4.8 . Weld on tab only (a) weld on disk only (b) 62
Figure 4.9 - Proper lateral location for weld ................................................................. 62
Figure 4.10 . Full penetration (a) and partial penetration (b) as viewed from the underside
of the joint .......................................................................................................... 63
Figure 4.1 1 . Close-up of top surface: g d (a) pinholes (b) ........................................... 64
Figure 4.12 . Close up of top surface imperfections: concavity (a) and convexity (b) .... 64
Figure 4.13 . Close-up of bottom surface: good (a) and pinholes (b) .............................. 64
Figure 4.14 . Close-up of bottom surface imperfections: partial penetration (a) and
excessive material ejection (b) ................................................................................. 65
Figure 4.15 . Scribing the tab number (a) . wet-saw used for sectioning (b) .................... 66
Figure 4.16 - Sections mounted in epoxy puck (a) information tag (b) ........................... 66
Figure 4.17 -Stereo zoom microscope with ring light (a) image from digital camera (b) 67
Figure 4.18 - Weld area measurement with the outline tool in lmage~roQ Plus .............. 68 Q Figure 4.19 . Thickness measurement in ImageRo Plus ............................................... 69 0 ............................................ Figure 4.70 - Single point measurement in Imageho Plus 70
Figure 4.2 1 - Geometnc properties of the weld area [83] ................................................ 71 0 Figure 4.22 . Hole measurement in ImagePro Plus ........................................................ 72
Figure 4.23 . Complete data file: 06-26- 16- 10- 16-S 1 ...................................................... 73
Figure 4.24 . Data file for a single tab: 06-26- 16- 10-16-S 1 -T 1 ....................................... 73
Figure 4.25 . Graph of original and interpolated data for a single sensor ........................ 74
Figure 4.26 . Graph of original and interpolated data at 5% of maximum signal value .. 75
Figure 4.27 -Approximations of a sinusoidai function using the MEM with different
number of poles [84] .............................................................................................. 76
Figure 5.1. Sequential training set (total error) ............................................................... 82
Figure 5.2 . Sequential tnining set (individual training error) ......................................... 82
................................................ Figure 5.3 . Sequential training set (individuai test error) 83
Figure 5.4- Results for randomized training set (total error) ............................................ 84
Figure 5.5 . Results for randomized training set (individual training error) ..................... 84
Figure 5.6 . Results for randomized training set (individuai test error) ............................ 85
Figure 5.7. Fully normalized training set (total error) ...................................................... 86
Figure 5.8 . Fully nomalized training set (individual training error) ............................... 86
Figure 5.9 . Fully normaiized training set (individuai test error) ...................................... 87
Figure 5.10. Two hidden layen (total error) .................................................................... 88
Figure 5.1 1 . Two hidden layers (individud training error) ............................................. 88
Figure 5.12. Two hidden layen (individual test error) ..................................................... 89
Figure 5.13. PSE (total error) ...................................................................................... 9 1
Figure 5.14 . Cornparison between PSI and PSE (total training error) ............................. 92
xii
Figure 5.15. Cornparison between PSI and PSE (total test emr) ..................................... 92
F i g u ~ 5.16 . Figure 5.17 . Figure 5.18 .
Figure 5.19 . Figure 5.20 .
Figure 5.2 1 .
Figure 5.12 .
Figure 5.23 . Figure 5.24 . Figure 5.25 .
............................................................... PSI (total error 40 hidden nodes) 9 3
PSE (total error 40 hidden nodes) .......................................................... 94
Cornparison between PSI and PSE (optimized total training error) ........... 94
Cornparison between PSI and PSE (optimized test emor) .......................... 95
PSE (individual training error) ................................................................... 96
PSE (individual test error) .......................................................................... 96
Cornparison between PSI and PSE (area training emor) ............................ 97
Cornparison between PSI and PSE (area test error) ................................... 97
Cornparison between PSI and PSE (lateral position vaining error) ........... 98
Cornparison between PSI and PSE (lateral position test error) .................. 98
Nomenclature
L-KH W-KH A-KH L-WP W-WD A-WP
length of the keyhole width of the keyhole m a of the keyhole length of the weld pool width of the weld pool width of the keyhole neural network input neural network weight neural network output neural network function maximum output value of a sigmoid function minimum output value of a sigmoid function initial value of a single input panmeter minimum value of a single initial input parameter maximum value of a single initial input panmeter normalized value of a single input parameter number of input nodes in a neural network number of output nodes in a neural network number of hidden nodes in a three-Iayer neural neiwork number of hidden nodes in a four-layer neural network target value for an output actual value for an output number of sarnples number of output varaiables
xiv
Chapter 1 Introduction
1.1 Background
The automotive industry has a significant impact on the Canadian economy, as i t
comprises a significant portion of the manufactunng GDP in Canada - (12.888 in 1998
[l]). More specifically. the province of Ontario is home to assembly plants representing
six different automotive manufacturers: Ford, Gened Moton. DaimlerChrysler, Honda,
Toyota and CAMI (a joint venture between GM and Suzuki). Parts rnanufacturen and
supplien alsc have plants spread throughout the province.
In the global marketplace. a Company must be able to produce a quality
component at a reasonable pnce to stay in operation. Cornpliance with quality standards
such as ISO 9001 and QS-9000 are now required for automotive suppliers to compete
intemationally. To remain competitive. companies must find ways of not only irnproving
part quality, but also reducing costs. Two of the most popular methods of accomplishing
these goais are automation and increased quality control. Automation is a popular choice
as it allows for the improvement in quality through the elimination of hurnan labour (and
thus human error), while simultaneousl y increasing production rates. Increased quality
control is another option as it can be implemented in both manual and automated systerns
1
to identify nonîonformances at various stages of the manufactunng process. The
availability of a wide variety of senson and monitoring equipment. dong with today's
high-powered cornputers. enables many different inspection and quality control
techniques.
1.2 Laser Welding in the Automotive lndustry
Laser welding of components is a highly automated process that is now
widespread in the automotive industry. In fusion welding processes. parts are joined by
heating such that the interface between the parts melts and mixes before cooling
(complete fusion) [2]. Cost savings and irnproved quality c m be achieved by switching
from traditional fusion welding techniques. such as resistance. MIG, and TIG, to high-
power laser welding (above 1 kW). Single-sided mess of lasers makes it possible to
create weld geometries impossible to achieve with conventional two-sided resistance
welding techniques. Ioining a piece of sheet metal to a hydroformed tube (Figure i. 1) is
an example of a single-sided weld geometry that is possible only with laser welding.
Section A-A
Top access only
Fipre 1.1 - Example of single-sided laser welding [3]
The cost savings possible with laser welding are achieved t h u g h increased
welding speed and decreased consurnables and downtime'. Some consumables found in
tnditional welding techniques are: copper tips in resistance spot welding, shield gas and
filler wire for MIG welding, tungsten elecrodes. shield gas and filler material for TIG
welding. For most steels. filler materid is not required for laser welding. Alurninum
components almost always require a tiller materid to ensure that the chernical
composition of the weld remains favounble. Research suggests that laser welding of steel
with no shield gas or a cheaper shield gas (COz vs. Ar) may be possible [4]. Despite the
advantages of laser welding, the initial capital expenditure to acquire a laser welding
system has delayed its adoption in some automotive applications.
1.3 Examples of Laser Welding in the Automotive lndustry
Laser welding has been used for the welding of transmission components for
more than 30 years. The laser welder. which replaced electron beam welders in this
application, is fixed while the cylindncal transmission components are rotated in order to
be welded. Electron bearn welden are powered by large transfomen. require thick lead
shielding and are usually opented in a vacuum environment. Laser welders, on the other
hand. are smaller and only require optical shielding.
Sheet metai welding is the fastest growing segment of laser welding usage,
beginning in the automotive industry in the late 1980s. m e joining of two or more pieces
of flat sheet metai to mate a railored blank before stamping is a relatively new approach
t The amount of cime a piece of equipment is out of service for tepair or replacement of consumables.
to the manufacturing of body panels and has been adopted by almost al1 of the major
automotive manufacturen. Typically, the weld is a stnight line. However laser
inteptors' increasingl y offer two-dimensional welding systems.
Figure 1.2 - Tailor welded blank and stmped part of a door inner panel [SI
The Auto/SteeI Partnership [ 5 ] h a identi fied major incentives for using tailored
blanks. Weight reduction cm be achieved by using thick material only where necessary.
such as a door inner where a thicker strip of material is located on the hinge side and a
thinner. lighter piece of material is used for the rest of the door (Figure 1.2). Part
elimination is achieved because extra reinforcements are no longer required, with the
added advantage of reducing die investment and assernbly costs. An increase in structural
integrity without weight gain results from a continuous part, which also improves the
dimensional control of the final assernbly. Better material utilization results as smaller
h laser integrator is a Company that combines an off-the-shelf laser with tmling and automation equipment of their own design for a rnanufacturing faciIity at a separate site.
pieces normally discarcied, such as window or door cuiouts from liftgates and bodysides,
cm be welded together to create a tailored blmk for another part.
The most recent use of laser welding in the automotive industry has been in the
joining of stamped parts. Attaching the roof of a vehicle to the rest of the body is one of
the more difficult applications of laser welding since it involves three-dimensional
geometries (Figure 1.3). Volvo. Daimlefhrysler and Volkswagen are a few of the
manufûcturen that have adopted this technoiogy.
Figure 1 3 - Roof panel l a x r welding at Volvo 161
1.4 Quality Control in the Manufacture of Laser Welded
Components
Many quality monitoring systems have ken. and are being. developed for laser
welding. Early quality monitoring systems solely relied on destructive testing of the
completed parts. This method was time consuming. expensive, and required dedicated
test equipment and personnel with only a fraction of the total production king inspected.
Non-destructive testing has reduced the need for. and frequency of, destructive testing but
has not cornpletely eliminated it. Tensile tests of conventional dog-bone shaped
specimens with the weld parallel or normal to the mis of the tensile specimen are used
for destructive testing. but represent only a limited number of possible forming
conditions [7. 81. For tailored blanks. numerous simulative tests adapted frorn standard
sheet metal formability tests exist to cover almost dl of the possible forming conditions.
The rnost common method is the hemisphencal punch test. in which a binder clamps the
blank at its edges and a punch is forced up through the middle until failure occun (Figure
1.1). The height and force of the punch at failure is recorded and used to compare various
materials and weld configurations. Variations within this son of test include the
placement of the weld line and the shape and size of the blank. binders and punches.
1 annular binder 1
blan k
hemispherical punch
Figure 1.4 - Hemispherical simulative forming test [7]
According to ISO 139 19- 1 (91 and the Auto/Steel Partnership [LOI, the quality
level of a laser weld in steel can be detemined by inspection of a cross-section of the
weld. The determining factors for qudity are: cracks, porosity, penetration, and the
shape of the weld. Undercut, mismatc h. concavity and convexity are important factors for
the shape of the weld (Figure 1.5. 1.6, 1.7). Typically, individual manufacturen also
impose their own additional quality cnteria.
I
z CAUCES UNDER 1 .Umm CAUCES 1 .Omm AND OVER (c 1 .Omm) (= or > 1 .Omm)
(YB) < or = 15% iY/X) c or = 20% (701 < or = 15% (ZBo < or = 2Wo !Y + Z X ) c a r = 15% (Y = Z XI< or = 20% W> or = 85% W> or = 8û%
Any material mismatch m a be added to c o ~ m i t y when dciemining the toul alldwable concavitv (e. K.. IZB(I + m e n t of mismatch < or = 15 pcrccni).
Figure 15 - Auto/Sieel Partnenhip concavity specifications [IO]
' E U A 1 AUG IS IMlLAR CAUCES *?ed h i e (UX) < or = Specified Value (Y + NX < or = Specified Value W c or = Spxilied Vdw of X for
Figure 1.6 - AutdSteel Pamiership convexity specifications [IO]
i s o 6520 reterenc
5Ot 1 5012
- 502
504
- 50 7
Useta for r w t r u n fat p8nU w i l d u l fiom on. SM.
l Lmirs for unp~ttectionr tor qullity Irv«s
n 5 0 . t ~ t or i mm. whi~nirivu Ir 1110
8rnd.r
h r O.! t or 0.5 mm. ~hichever 11 me amiUet
n 5 O.CS t 31 0.5 mm. wn~cnevrr I S :hc srnoilcf
h dO.2mm ~ 0 . 3 1 w !Ï Inni. whtcnma 4 mm mallu
h s 0.25 t or 3 mm. W~UCIUUCI 1s tne smi i tac
h i; 0.2mt-n + O.3t w 5 mm. W N c l w w a r is
Vir m Y k r
h 5 0 . 2 m m t 0.21 01 5 mm. nnichavsr 11
tha rnuüer
Fipre 1.7 - Section of ISO 139 19-1 describing weld quality[9]
II sO.Zrnm r C.151 or 5 mm. wniChru*t ir tne srnuiof
h 5 O.2mm r O . t t or 5 mm. -vmch~var 8s Ihe srnailsr
Tns iimu ida i i to d i v i r w n r lrom t f u correct pasilion. Unims otnuwiio a p m f i d . rni cocrecr poscimn IS tnrr w h u i ;ho cintr.(inn Coinada.
In addition to destructive test methods, there is a large number of non-destructive
quaiity assurance methods available. in generd. these non-destructive methods fa11 into
three categories: pre-pmcess, pst-process and in-process. Re-process techniques use
specialized tooling (precision shear, follower wheel, SOUKA' profile roller) or carneras
8
h s G . 2 r n r n 0 . 1 5 ~ w 5 mm. wbc.?ovcr 1%
:ni smuitr
l l J
I
to cornpensate for excessive gaps in the joint that would affect the quality of the weld.
Post-process systems may use laser profilers to determine the surface geometry of the
weld while intemal defects can be identified using magnetic and ultrasonic detection
methods. In-process techniques use sensors such as microphones, photodiodes, and
cameras to monitor the in-process phenomena o c c h n g in the weld pool [l i l .
Microphones are used to record acoustic emissions from the weld pool. Photodiodes are
used to mesure the radiation emitted from the weld pool. Optical techniques are used to
view the size and shape of the molten weld pool. Data from one or more of these sensors
h u been used. with varying degrees of success, to solve the problem of laser welding
process control. A thorough overview of available quality monitoring systems for laser
welding is presented in Chapter 2.
Given the a m y of sensors thsi cm be used for laser welding, there is a wedth of
data that can be generated. However. the number of techniques and rnethods available to
analyze and interpret the data are relatively limited. Statistical techniques are the most
common approach for data processing. Absolute maximum and minimum values have
ken used to indicate major problems, but are of limited use on noisy or drifting signals
(Figure 1.8a). Tolerance limits around a baseline signal have ken used to compensate for
dnft (Figure 1.8b) [12]. The deviation from the mean value of a noisy or rapidly
fluctuating signal has been used to identify minor defects [13]. Cornparison of the data to
an approximation denved from non-linear curve fitting is another statistical technique
used to interpret sensor signals [12].
(a)
Figure 1.8 Absolute values (a) and tolerance limits around a baseline signal (b) as statistical
techniques for signal processing
Physicai models have been developed for certain aspects of laser welàing, but no
genenl goveming model incorporating al1 the physical phenornena involved in the
process exists. A heat conduction model has been used to estimate weld penetration depth
[14] and a model has ken used to predict optical and acoustic emissions from the weld
plume [LSj.
For a system such as laser welding. accurate and comprehensive physical models
do not exist, experimentai models are the only option. One method for developing
experimentd models is through application of Neural Nenuorks. Neural networks cm be
used to model any input-output relationship [16]. No knowledge of the relationship
between the input and the output is required as this relationship is detennined during
training.
1.5 Objective
The objective of this work is to use a neural network to predict the shape and
location of the fusion zone in gear welding of automotive transmission parts. The system
developed would rely on senson sampling plasma in three ranges: ultra-violet, visible
and infrared.
1.6 Outline of Thesis
This thesis has been organized in six chapten. Chapter 1 is a general introduction
and a brief summary of laser welding in the automotive industry. Chapter 2 provides a
technicd background on the laser welding process and a detailed summary on the
techniques used to predict the quality of the weld. This chapter identifies where
opponunities exist to irnprove existing quali ty monitoring s ystems.
An outline of the approach used to modify an existing system with the goal of
improved performance is presented in Chapier 3. The selection process for the
experimental equipment and theoretical information on the solution technique is also
presented. Chapter 4 describes the equipment selected, the experiments performed and
the data manipulation used dunng this research. Expenmentai results are presented in
Chapter 5, while Chapter 6 contains a summary and discussion of the results dong with
recommendations for future research in this area.
Chapter 2 Literature Review
2.1 Introduction
The increase in the use of lasen for welding in the automotive indusiry in the last
10 years. combined with more stringent quality standards. has resulted in the
development of quality monitoring systems for laser welding. A generai understanding of
the phenornena preseni during the welding process and the types of lasers in use is
required before the benefits and limitations of existing quality monitoring systems can be
appreciated. It is also important to be aware of the quality measures used in the different
applications of laser welding.
Quality monitoring systems have k e n implemented at different stages of the
welding process. A variety of sensors and techniques have k e n used to gather
information for these systems. improvements in current signal processing techniques
have the potential to improve the usefulness of quality monitoring systems in laser
weiding operations.
2.2 Laser Welding Process
Welding in which an incident laser beam is absorbed by the surface and the
material is melted through the conduction of heat is called conduction welding. Keyltole
welding occun when a tightly focused spot of laser energy vapourizes the material,
creating plasma and a hole. melting the surrounding material (Figure 2.1).
71 0 Weld Area
..
Figure 2.1 - Schemtic of conduction (a) and keyhole (b) welding
Greater penetration depths are possible with keyhole welding as compared to
conduction welding. For a full penetration weld, the profile of the keyhole weld has
almost panllel edges whereas a conduction weld is clearly tapered (Figure 2.2).
- -
(a) (b)
Figure 2.2 -Cross section of conduction (a) and keyhole (b) full penetration welds [17]
The lasing material used to generate the radiation determines the type and name
of a laser. Three distinct types of lasers are used for welding in the automotive industry
(Figure 2.3).
Lasina mitwirl CO2 #as Nd:YAG
Wavdongth [)ni] 10.6 i 06 - -
Stimulith HigMnquency Flastibulbs. electnc
Ma*, &put [kW Up to 4 0 Up to 4 (regufated)
intrnrity w/cm'j 10' - 10' 10' - 10'
Ilmm parimetm 5.4 (700 W) c 5 (200 W) product [mm m d ] 13.5 (20 kW) < 25 (4 kW)
Berm guidance Mirrors Opticai fiben i
Maintsnrnta ~pprox. 1000 inteml nil 1 Approx lQW 1 (bu~bs)
Semicanductor crystal
Oirect current diodes
Dimt or optical fiben
Approx. 100
Figure 23 - Laser types and their c haractenstic parameters [ 181
The COz gas laser is the oldest and most commonly used laser technology in the
automotive industry. It is available with power outputs of up to 100kW. High frequency
excitation is used to generate the COz laser beam with an electrical to optical efficiency
of approximately L0%. Liquid cwled copper minors are used to guide the long
wavelength light to the workpiece. The path of the laser barn is enclosed and flooded
with an inert gas (typically helium) in order to prevent contamination of the mimors and
loss of laser power. The beam of a CO2 laser has a charactenstic gaussian profile ( Figure
2.4).
Radial Distance Radial Distance
Figure 2.4 - Characteristic berm profiles of COr (a) and Nd:YAG (b) lasen
Neodymium Yttrium Aluminum Gamet lasers (Nd:YAG) are solid state lasers
that are available with enough power (up to 4KW) to cut and weld automotive
cornponents. Nd:YAG lasers have a low electrical to optical efficiency (approximately
5%) due to flash lamps which are used to transfer energy into the solid crystal lasing
medium. Because they emit shoner wavelength light and only require liquid cooling,
Nd:YAG laser beam cm be transmitted through fibre optic cables. This allows for a small
laser output unit to be located away from the source, making it well suited for integration
with robots. Nd:YAG has a higher initial equipment cost than COz, but this cost can be
partially offset by its reduced consumption of gases. This is especially true in European
countries where the gases must be imported. Because of the shorter wavelength and the
top-hat profile of the bearn, better coupling of the incident energy to aluminum occurs
with Nd:YAG lasen compared to COz lasers ( Figure 2.4) [19].
High-power diode lasers are the newest lasers in the automotive industry. These
lasers use hi& efficiency semiconductors, which are virnially maintenance-free, to
15
generate the bearn. The wavelength and beam profile are sirnilar to those of Nd:YAG
lasers. The diode delivery unit is sufficientiy compact that it can be direct mounted above
the workpiece eliminating the need for mimrs or fibreoptic cables for delivery. The
eficiency and size of a diode laser may make it the system of choice in the near future.
2.4 Laser Weld Monitoring
Non-destructive weld quality monitoring systems fa11 into three distinct
categories: pre-process, pst-process and in-process. Re-prwess inspection systems
provide an opportunity to adjust the systern before welding, but are lirnited to dealing
with part fit-up issues. Post-process inspection systems address the overall quaiity of the
final product before king delivered to the customer and are an excellent tool for
statistical process control. Although post-inspection results can be used to correct
problems in subsequent parts, they cannot recover already defective parts. In-process
inspection systems are used during welding and cm be incorporated within an existing
system without requiring an additional stage. The addition of a quaiity control system to a
laser welding machine cm increase producüvity by quickly identifying and reducing the
resulting number of defects in a process.
Component geometries. packaging and mounting constraints often detennine the
type of monitoring system used. Tailored blank welding machines typically have the
space to provide the access required by most inspection systerns. Welding systems for
transmission components and completed body panels often have blind welds or
geomevies that offer no access to one side of the weld, which eliminates certain types of
inspection systems.
2.5 Pie-Process Monitoring
Part fit-up before welding, which is crucial for dl welding processes. is
particularly important in laser welding where extra material is not added with a filler
wire. Rigid control of the location of the parts by mechanical methods is typically used to
ensure the quality of a joint before welding. Circular parts found in gear welding are
press-fit to maintain the desired tolerance between components. Clamps andor a follower
wheel are used with sheet metal to minirnize any gaps between parts in the weld area
(Figure 1.5).
Figure 2 5 - Follower wheel for component welding [20]
S heet metal (Omrn) used in tailored blanks requires special attention to edge fit-
up as good edge quaiity ailows higher welding speeds and fewer quality rejects.
Maintaining a consistent gap over the length of the weld is aiso crucial. A precision shear
can be used to trim a small amount of material from the mating parts before welding.
Straight-cut edges to a tolerance of +/- 0.381m.m over a 2.25 meter length, can be
guamteed with a precision shear system [21]. Using a laser to trim the blanks c m also
generate the straighmess required. Laser blanking can be a costtffective solution for low
volume applications as it elirninates the need for numerous blank dies and a precision
shear [22].
An alternative approach. developed by SOUDRONIC" of Switzerland, uses a
three-step process for edge preparation before welding. Using a spring-loaded hold down
wheel ( S E E ~ d e r ) on the thinner blank, a second pneumatically loaded wheel
(SOLXA' roller) deforms the edge of the thicker blank ensuring a close fit before
welding (Figure 1.6). The remaining gap is rneasured by a canera (SOUVIS 1') and used
to control the laterd location of the laser spot [23]. Pre-process monitoring of laser
weldcd components vvies depending on part geometry and the equiprnent k ing used. In
some cases pre-process monitoring is not required.
Spring loaded downhold roller Pneumatically Ioaded SOUKA@ (SEEMT profile-roller
Magnetic bar : Support roller
Fipm 2.6 - S O ~ R O N I ~ edge preparation system [23]
2.6 Post-Process Monitoring
ISO standard 13919-1 emphasizes surface geometry issues such as concavity and
convexity as well as intemal defects. such as pinholes [9]. There are a variety of non-
contact inspection methods that are able to map the surface of the weld and detect
intemal problems.
For tailored blanks. laser triangulation profiling is commonly used to inspect the
geometric profile of the weld [23. 24. 251. Single or multiple laser diodes project a line(s)
of light across the weld surface and by viewing the reflection at an off-angle. the depth
across the weld c m be recorded by a photosensitive device (Figure 2.7). Convexity and
concavity in the weld region and height rnismatch of the individual blanks are the rnost
common features investigated by these systems. When part geornetry allows. the bottom
surface of the weld cm also be used for quality purposes. The resolution required to view
small imperfections such as pinholes is currently available in some of these optical
s y stems [NI.
LIGHT PLANE Orthogonal Iiter rtripe and
biingulrtion planer
Figure 2.7 - Laser triangulation principle for profile acquisition [24]
Optical scanning for surface imperfections is of limited use on deep welds where
interna1 defects determine the quality. X-ray systems that do not require a vacuum
environment can generate images of intemal defects. but have limited use for parts wi th
geometries that do noi allow two-sided access.
2.6.2 Ultrasonic
Ultnsonic testing methods have been successfully applied in resistance welding
to determine the size of the weld nugget. The presence of inclusions and porosity can also
be detected (261. Proper coupling between the sensor and inspected material is required
and this is usudly accomplished by using a liquid or gel transmission medium. Rough
surfaces andor irregular geometries cari affect coupling and thereb y, the reliabili ty of
ultnsonic techniques [27]. Ultrasound sensors cm be used on a variety of materials,
including steel and duminum. UltraSound sensors in a gear welding unit were determined
to be of liale use in monitoring part quality and were not recomrnended to be included in
future units.
2.6.3 Ultrasonic & Magnetic
Electromagnetic Acoustic Transducer (EMAT) combines magnetic and ultrasonic
technologies to eliminate the need for a medium between the sample and the inspection
system found with pure ultrasonic systems. Coils mounted under a permanent magnet
induce altemating eddy currents in the welded material generating ultrasonic waves that
are reflected by defects (Figure 2.8). Cornparison of the generated waves to the reflected
waves is used to detect the presence of defects in the material [ B I . It is claimed that
EMAT technology is able to detect both surface and interna1 defects such as lack of
penetntion and porosity or voids [29]. EMAT is limited to applications in steel and
cannot be used with aluminum due to its low magnetic permeability.
Figure 2.8 - EMAT defect detection [28]
2.6.4 Magnetic
In a ment study. Magnetic Flux Leakage (MFL), which relies only on magnetic
interactions, was shown to be a prornising technology for steel tailored blanks. MFL-
based systems are already used to inspect for corrosion and cracking in welded steel
pipelines [30]. A high power magnet with ifs pole pieces bndging the weld saturates the
sample with magnetic flux (Figure 2.9). A Hall effect probe is used to measure the level
of flux above the arer of interest. By subtracting background levels, the system is able to
detect any excess leakage of flux caused by interna1 or extemal discontinuities in the
sample. Tests using MFL have also shown similar performance to that claimed using
EMAT [3 1 1.
Figure 2.9 - Schematic of Magnetic Fiux Leakage apparatus 1321
2.7 In-Piocess Monitoring
There are two methods to mount in-process senson in a laser welding system
(Figure 2.10). Co-axial mounting uses special mirron andfor lenses to view the weld
area dong the incoming laser bem. Extemal mounting allows off-angle views to be
used. Packaging and access issues typically govem the mounting method. Extemal off-
angle mounts have k e n shown to give larger sipal values, but CO-axial mounts are
easier to incorporate into systems and always have a clear view of the area of interest
WI.
Figure 2.10 - Two methods to mount in-process senson
2.7.1 Cameras
Unlike human inspecton, vision systems are able to resolve small regions and can
function in the intensity of light present in the weld. Weld pmperties visible in images
from in-pmcess cameras include the size, shape and intensity of emitted radiation. The
size and intensity of the weld pool has ken successfully used to control the welding
speed in tailored blank welding [34]. The shape and intensity of IR radiation emitted
from the weld pool has been used to control the laterally focused position of the laser in
both tailored blank [35] and transmission gear [36] welding. The length (L), width (W),
area (A), relative position (LW) of the keyhole (KH) and the weld pool (WP) are the
geometrical properties used to control the laser power in a transmission gear welder
(Figure 2.11).
Figure 2.11 - View of the molten pool surface; a) Sketch of the geometncd properties. b) Non-
disturbed vide0 image, c ) Video image of part misaIignment. [36]
Cameras are available with two types of sensors to convert light images to
electncal signals: Charged-Coupled Devices (CCD) and Complementary Metd Oxide
Semiconductors (CMOS). The elements in a CCD camera operate like capaciton, storing
the incident photons for a predetermined length of time. The current output is
proportionai to the intensity of measured light. A CMOS camera is based on photodiodes
serially connected to a resistor. The voltage output of a CMOS camen is logarithmically
related to the incident intensity. The speed at which both cameras generate full-frame
images is relatively slow at 50 to 60Hz. lirniting their usefulness for most industrial
applications. Even high speed cameras operating at 4ûûHz cannot reveal certain
instabilities commonly found in laser welding [37]. Lasers that operate in pulsed mode
are comrnon in drilling applications and require higher speed cameras than continuous
mode applications such as welding. Increasing the scan speed of a CMOS camera from
66Hz to llcHz is possible by selecting only a smdl portion of the full array for an image.
Claims have k e n made that a higher dynamic range can be achieved using a CMOS
carnera rather than a CCD camera [38]. An increase in the speed of a 30Hz carnera to
3kHz has k e n accomplished by sequentially scanning a full an-ay that was divided into
LOO separate m y s [39]. Speeds of 20kHz are theoreticdly possible by combining
images frorn l ine-my scans of the molten pool [40]. The increase in speed from
scanning a single line is achieved by sacrificing the area and shape information of the
weld pool in favour of intensity values.
2.7.2 Acoustic
Acoustic emissions emanate from the weld pool as the generated vapour displaces
the arnbient air and can be detected using an externally-rnounted microphone [15].
Precise placement of the microphone is generally not required as the sound waves
emanate in al1 directions. Some of the results of using acoustic sensors in laser welding
included:
A strong nlationship between acoustic emissions (over the 6-17 kHz range) and
the welding speed at constant laser intensity was shown [4 11.
An intense, narrow band of acoustic emission was found to be present in a high
quality weld as compared to a poor quality weld, which has a broad band response
acousticai spectrurn [42].
Five band-pass filters (0.3-1.3, 1.2-1.9. 3.0-3.8, 6.9-7.6 and 8.0-8.8 kHz) have
k e n used to segment the acoustic output from a production air bag canister laser
welder to predict final part quality 143).
Acoustic output has k e n incorporated into a closed loop control system to control
the focal height of the laser in a laboratory 1441.
Background noise from automated equipment in a production environment can
present problems in obtaining a clear acoustic signal required for analysis. Careful
selection of specific frequency ranges cm minimize the influence of outside noise
affecting the signal.
2.7.3 Acoustic and Carneras
Incorporation of a camera and microphone into a single monitoring system cm be
used to overcome the dnwbacks of the individual systems. The broad frequency
response of an acoustic system augments the slow analysis speed of most carneras.
Observing the small weld m a with a camen reduces the influence of extemal noise
captured by the microphone. A CO-axial CCD carnera with an accuracy of +/- 12% has
been used to increase the accuracy of an off-axis microphone from +/- 25% to +!- 10% in
the on-line monitoring of penetntion depth [45].
2.7.4 Optical Emissions in Laser Welding
Three bands in the optical spectrum are of particular interest in the laser welding
of steels: infrared (IR), ultraviolet (W) and visible. The molten pool surrounding the
keyhole emits IR radiation and the plume that foms above the keyhole emits both W
and visible radiation [48]. When material is vapourized dunng keyhole welding, the
elements are excited and emit distinct signatures of radiation across the optical spectnun.
A spectrum analyzer is capable of dividing al1 the incoming light from the weld
pool into its constituent wavelengths, which a multielement CCD detector c m then
analyze in real-time at a speed of up to 125H.z [46. 47. 481. With steeis, the ernitted
spectral radiation of the weld reveals three iron atom peaks that cm be used to determine
the piume temperature and its variation for process control [46]. It has been shown that,
when joining dissimilar metals (for exampie copper to steel), spectral andysis of the weld
plume can be used to track the location of a butt weld's seam or the penetration of a lap
weld by analyzing the compositional components of each material 1471.
2.7.5 Photodiodes
A photodiode cm measure the iniensity of ernitted radiation from the weld pool.
Depending on the type of photodiode, it cm be sensitive to a broad or n m w band of the
optical spectrum. Opticd îllten can be used with broad band senson to lirnit the
wavelengths of light transrnitted. Grouping photodiodes such that they are sensitive
across the optical spectrum cm act as a simplified spectrum analyzer. Size and shape
measurements. which c m be made with cameras, are not possible with photodiodes.
However, with multiple photodiodes, the radiation from several different areas of the
weld pool can be viewed. The frequency response of a photodiode (3 kHz) is such that it
can detect instabilities in a weld that a high-speed camen cannot [37]. The sampling
speed of a photodiode is determined by the data acquisition system.
2.7.5.1 Placement of Photodiodes
Different methods exist to rnodify the copper rnirron used to redirect laser light in
CO2 welâing to gather the CO-axially reflected process radiation:
A tuming mirror with a precisely positioned small hole could transmit a srnall
amount of reflected light to a properly placed sensor.
A dichroic minor could allow a portion of the reflected radiation to be
transmitted straight through to a sensor but would not transmit the high power
incident laser beam.
A diffractive mimr is a standard tuming mirror that di rects a small
percentage of the laser radiation at a different angle towards a sensor with a
small diffractive stxucture machined on its surface.
A scraper mimor could be positioned outside of the main incident high-power
laser bearn and would only gather the reflected radiation by one of two
methods:
- A large parabolic minor with a hole the size of the incident laser bearn
will gather reflected light from around the incident bem;
- A small rnirror on one edge of the incident laser beam will gather light
from a specific spot near the keyhole.
A small scraper mirror is advantageous in that it does not reduce the incident laser power
like a special tuming mimr and its alignment and spot location can be changed without
modification to the welding optics.
1
process radiation
dichroic mirror converging optics
mirror with hole
focussing mirror.
Figure 2.12 - Different CO-axial mounting techniques [L2]
2.7.5.2 Infrated Photodiodes
Molten material in the keyhole radiates in the IR range with wavelengths of lighi
from 700nm to 1700nm. An arrangement consisting of one vertically-mounted IR
photodiode aimed in the keyhole, in conjunction with a second side-mounted photodiode
focused on the plasma plume have been used to detect full-penetration welding in sheet
steel [49]. A single CO-axially mounted IR photodiode that used a scraper mirror to gather
radiation from the weld region has ken shown to be capable of determining full
penetration in various sheet materials [i3]. A sigificant drop in the DC level of a signal
from an IR photodiode is a good indication of full penetration as a portion of the plasma
plume escapes through the bottom of the pan. The AC component c m be used to indicate
alignment and contamination problems [49,13].
2.7.5.3 Visible Photodiodes
Visible radiation with wavelengths of 4ûûnm to 700nm can aiso be monitored
with photodiodes. The plumes during high power CO? laser welding of steels (20kW)
have been shown to emit strong DC signals in the blue region (350nm-500nm), whereas
keyhole plasma have strong signals in the green (5ûûnm-600nm) and red regions
(6ûûnm-720nm) of the optical spectnim [SOI. A correlation between blue and IR
radiation intensity from the plasma to the cross-sectional geometry of a butt weld in sheet
steel has been found with two extemally-mounted photodiodes [5 11.
2.7.5.4 UV Photodiodes
W radiation with wavelengths of 260nm to 40nm is the third region of the
optical specûum that is andyzed with photodiodes. Both W and IR photodiodes have
been used to increase the amount of information from the weld and have been used in
severd monitoring systems [51,53,54.55]. When UV variations from the plume occur at
the same tirne that the IR signal from the size of the weld pool is constant. it can be an
indication of keyhole instabilities or failure. which can then be used to predict the
transition to conduction welding [52]. Using the UV radiation from the plume and an IR
sensor focused on the front edge of the weld pool. the size of the gap and the resulting
weld quality cm be determined from the IR deviation. The size of the gap can then be
used to control the focal position in order to ensure a quality weld [53]. IR and visible
radiation from the weld pool viewed CO-axially have also been used to control focus
height during welding [Xi]. When two-sided access is possible, as is the case with
tailored blanks, multiple photodiodes aimed at the top and bottom weld surfaces can be
used for detecting full penetration welds [37]. When hl1 penetration is achieved an
increase of W emissions occurs on the lower si& accompanying a decrease on the top
due to plasma escaping from below. Conventional laser lap welding of zinc-coated steel
requires a constant gap to be maintained between the mating parts to allow the zinc
vapour to escape sideways. Otherwise, the zinc vapour becomes trapped in the weld to
create a bad weld. Coaxially rnounted IR and extended range UV (350nm-700nm)
photodiodes are used to adjust the focal position and monitor the weld quality in lap
welding of zinc-coated steels by observing only the weld plume above the surface [ S I .
The use of a gap and the trapping of zinc vapour c m be avoided by using a new method
of lap welding that substantially tilts the incident leading angle of the laser in order to
allow the zinc vapour to escape venically from the keyhole [57].
2.7.6 Photodiodes and Optical or Acoustic Sensors
The small sire and inexpensive cost of photodiodes have lead to their integration
with many of the other techniques described earlier in this chapter. Microphones and
photodiodes are boih simple sensors with signals that can be sampled at high speed.
making ihem an excellent choice for pulse welding [58]. Cameras that view the molten
weld pool have been augrnented with the addition of quick responding photodiodes,
enabling a quality monitoring system to detect smail imperfections transparent to systems
using a camera only (Figure 2.13) [37]. A multi-sensor approach. using multiple
photodiodes and acous tic sensors (each with particular advantages and disadvantages),
has been determined to be the best approach for real-time monitoring of laser weld
quality [59].
Figure 2.13 - Extemal mounting of photodiodes and cameras in a single quality monitoring
system [37]
Infrared and UV photodiodes in combination with a microphone were used on a
transmission gear welder to detect adequate and inadequate weld penetration [60].
Results of 100% accuracy for the classification of full penetration are claimed for the
system when implemented at an industrial site. This is of little consequence, as a single
top-mounted photodiode has been shown to be able to detect the transition to full-
penetration weld by a sharp drop in signal Ievels corresponding to a portion of the plume
escaping from below the weld (371. Research has also shown that it is possible to detect
the transition from keyhole welding to conduction welding by looking at the frequency
distribution of optical and acoustic ernissions from the weld in the frequency dornain
instead of the time domain [6 11.
2.7.7 Other Sensors
The high-energy plasma of the plume generates a potential difference between the
laser nozzle and the materid king welded. Variations in the mobility of a number of
charge canien (ions and electrons) give a potential difference across the plume (Figure
2.14) [62]. Considenng the presence of metallic particles, it is possible to measure the
conductivity variations in the plume with a metallic ring surrounding the plume 1581. The
potentiai difference and the conductivity cm be used to estimate the size and fluctuations
of the weld plume. as well as the quality.
Figure 2.14 - Rinciple of potential in the weld plume [62]
The capacitance of the air gap between the laser output and the component being
welded can be used to monitor the focal heighi. A capacitive nozzle was specifically
designed for this task and was used to successfully conml focus height in conduction
welding, but gave false ~adings during keyhole welding due to the presence of plasma
163 I -
In another study, X-ray transmission observation was perfomed during welding
and it reveaied information regarding the formation mechanisms for porosity [64].
However. this required specialized laboratory equipment impracticd for the production
environment.
2.8 Signal Processing
Interpretation of the wealth of data which can be gathered from the m y of
available sensors is made more difficult by the non-linear and chaotic nature of the
welding process [65.66]. Three different approaches to process this data have been used
in attempts to control the welding process: statistical methods, fuzzy logic and neural
networks.
2.8.1 Statistical Methods
Statisticai methods are useful for interpreting data recorded from vision systems
and other systems that contain relatively smooth data. Setting upper and lower thresholds
on sensor signals that correspond to weld quality thresholds îs the simplest of these
methods. However. such absolute bounds tend to be unreliable when the signal is noisy or
fluctuates rapidly (as is found with microphones and photodiodes). Means and standard
deviations of signals have been used to address this problem [67]. Analysis of the
deviation from the centroid of a cross correlation plot between two senson has ken used
in the Weld Process Monitor. a multi-sensor system from Powerlasers ATC~, to estimate
' Powerlasen .4TC. 543 Mill St.. Kitchener. ON NZG ZY5 hap:\\www.luaintegrator.com.
the joint quality. A Kalman filter, which is a recursive least squares approximation, has
k e n used with photodiodes to control the focus height in laser material processing, but
required good initial starting conditions to operate accurately [68]. Non-linear regression
has ken used with some success to predict either partial or full petration using two W
senson and one iR sensor, but the system failed to predict both in a single mode1 [69]. In
general. it has ken shown that variation in amplitude of the time domain signals is a
highly unreliable measure of quality as compared to the distribution of the sarne signals
in the frequency domain. [67. 701. Linear discriminant analysis of fiequency domain
signals from senson has been used with some success to develop functions for predicting
weld quality with reliability above 85% [7 11.
2.8.2 Funy Logic
Fuuy logic is becoming a popular method to interpret sensor data in laser
welding [53, 541. A membership function is used to describe the output of a mode1 that
allows an uncertain answer. thus making the exact outputs fuzzy (Figure 2.15). The
equations used c m be determined through experimentation, theoretical analysis or expert
opinions.
Figure 2.15 - Example of a membership function that allows an uncertain answer to a Boolean
equation
A general relationship function for the system of equations is calculated by
summing the individual outputs. A final equation lwks at the fuzzy result in order to
determine a precise outcorne. For exarnple, a fuzzy logic system that has three equations
may require that only two of the three agree for the output to be me. Tolerances are
applied to the membership functions enabling fuzzy logic to handle noisy and fluctuating
data. One of the disadvantages of this system is that a set of equations for the system
must be generated and can vary between applications. The validity of the equations
depends on the skills and knowledge of the user.
2.8.3 Neural Networks
Similar to the way that the human brain processes information. neural networks
do not require any functional knowledge of a system. The relationship between input and
output is learned by the network through repeated presentation of data in a process called
training. Training sets are comprised of data with hown input and output values
representing the range of data to which the network will be exposed. A neural network is
comprised of numerous connected pmessing elements called neurons. Neurons cm have
36
multiple inputs with diffenng values, but only a single value can be output (Figure 2.16).
Connections between the neurons are weighted and these weights are determined during
training from initidly random values. A continuous and differentiable function with an
input range between -o, and +oo, an output range between O and I is typically selected for
the neuron. with the output value king determined after the surnmation of the individual
inputs. The number of neurons and their arrangement is variably dependent on their use.
and is govemed by the number of inputs and outputs in the system.
1 inputs weights output
Figure 2.16 - Model of a single neuron 1721
Static neural networks are trained prior to operation and do not Vary dunng
opention. A disadvantage of siatic neural networks is that uaining data is required prior
to operation. Static neural networks do not perform well in systems where the input data
may drift or fa11 out of the training set, thus highlighting the importance of the range and
quality of the training set.
A dynamic neural neiwork lems during operation and is usefùl for interpreting
rapidy changing signal deviations. An advantape of dynamic neural networks is that they
do not require training data so that they can be used with unseen data. Dynamic networks
are capable of interpreting noisy input data.
There have been many applications of neural networks in laser welding for
processing sensor signais. Neural networks have been shown to be capable of detecting
full penetration and excessive gaps with a 98.5% and 99% probability of success,
respective1 y, when viewing keyhole plasma from the bottom of the weld in the frequency
domain [73]. Neural networks have also k e n used to automatically optimize the focal
point height using two extemally-mounted photodiodes aimed at the top and bottom
surface of the weld on a single plate [74]. This was accomplished by training the network
to detect proper penetration and then verified by letting the neural network vary the
height until the proper focal position was reached. Penetration depth estimation was
better for neural networks ihan non-linear regression for full and partial welds, (accurate
within 5% venus 35%). using UV and IR sensors above the weld [69]. In transmission
gear welding, it is sufficient to know that full penetration has occurred due to the
relatively large size of the weld when compared to tailored blanks. In cases where large
weld areas are present. a quality monitoring system should be able to determine the size
and shape of the weld pool and whether any porosity is present throughout the area.
2.8.4 Combinations
Combinations of neural networks and fuvy logic have been used to overcome the
individual disadvantages for signal pnxessing in laser welding 172, 751. An on-line
neural network was combined in parallel with a fuzzy logic system to enable the
complete systern to adapt to "unusual" or "out of the ordinary" instances that may be
present in the incoming data Stream from three photodiode signals 1751. The same neural-
fuzzy approach was used in a system using one sensor to identify unusual events and
check welding parameters. whle a second sensor was used to masure the gap [34].
The use of a static and dynamic n e d network in a serial fashion has been used
to successfully monitor on-line tool Wear in tuming. but has yet to be applied to laser
welding [76, 771. In this system, an adaptive neural network was created and the
dynamic network was able to adjust the system when the test conditions were varied
outside the vaining set parameters of the static network.
2.9 Conclusions
There are many different methods for gathenng information about laser welds.
Although information that is gathered before and after the welding process is important in
order to produce and inspect cornponents. it cannot be used to control the laser welding
process. Different in-process sensor arrangements have been used to control various laser
parameters such as focus height and Iatenl focal position, and have been incorporated
into quality systems. The availability of a signai processing technique that cm predict
weld quality over a wide range of values is limited.
In a production environment. quality monitoring systems are an important
component of a laser welding system as they provide vaiuable information on the
performance of the process. The size and the shape of the weld, as determined from
destructive tests, is the most diable method to rneasure the quality of a laser weld. Non-
contact photodiodes are one of many in-process inspection techniques that cm be used to
rnonitor phenornena emitted from a weld p l . Current signal processing approaches are
unable to relate the sensor signals to the shape and area of the weld. A quality monitoring
system that incorporates neural networks for signai processing may be possible to relate
in-process sensor signals io the cross-sectional geometry of a laser weld.
Chapter 3 Methodology
3.1 Introduction
Quality standards for the manufacture of laser welded components. as discussed
in Section 1.4 of Chapter 1. use the size and shape of a cross-section of the fusion weld
zone as a mesure of quality. The literature review in Chapter 2 indicated that there are
many different types of sensors which c m be used to gather information dunng laser
weiding. Ir also found that there exist few methods to relate the gathered sensor data to
the overall quality of the weld. No methods were found that were capable of relating the
size and shape of the weld to the gathered sensor data. However. the literature review
indicated that it was feasible to use neural networks to correlate the quality of the weld to
in-process photodiode sensor ciaia. The most comrnon class of neural networks. the multi-
layer feedforward network, was selected to be used in this research [78]. The feedfonvard
network is cornmonly used for pattern recognition, function approximation and
forecasting.
3.2 Y ultilayer Feedfonnrard Neural Networks
Neurobiologists have developed theones conceming how the cells in our brain
operate and communicate. Artificiai neural networks are mathematical models based on
the observations of the human brain and were invented in 1943 by McCulloch and Pitts.
Practical limitations in the late 1960s decreased funding for reseuch in neural networks.
Most of these limitations were resolved by the late 1980s and the use of neural networks
is increasing. [78]
A feedfonvard neural network is compnsed of multiple layers of decision making
nodes called neurons. The fiat and last layers are the input and output layers respectively.
and they are the only ones connected to the outside world. Usually a minimum of one
hidden layer is located in the rniddle. In a feedforward network, each input node is
typically connected to every input in the first hidden Iûyer and every output of the first
layer is connected to every input in the subsequent layen ending at the output layer
(Figure 3.1).
Figure 3.1 - Typicd multilayer feedfonvard neural network architecture (781
It is cornmon to refer to a feedfonvard neural network by the number of nodes
present in each layer. For example, Figure 3.1 would be called a 3-2-3-2 network
42
indicating 3 inputs and 2 outputs with 2 hidden layen of 2 and 3 nodes respectiveiy. Each
neuron can have multiple values for inputs but only one value cm k output (Figure 3.2).
The outputs of the nodes are modified by weights before becorning inputs to the next
layer. The connections between layen modify the value of the output signds through a
set of weights.
Figure 3.2 - Neuron with multiple inputs and a single output (781
Guidelines exist to suggest the number of hidden layers and the number of nodes
in each layer. but individual values for a given network need to be determined
experirnentally. Time for training increases with the number of layers and nodes in a
network. If too many nodes are selected, the system may leam specific values and not be
able to generalize a result. The recommended method for selecting the number of nodes
is to start with a small number of nodes, one or two, and increase the number until the
desired performance is reached or no irnprovements in e m r occur. It has been show that
a neural network with one hidden layer cm approximate any function with a certain
degree of error [16]. In practice, the amount of training required can be reduced in
functions with discontinuities by using a network with a maximum of two hidden layen
[79,80]. These networks are called universal approximators.
The input signals, xi, are modified by the weights on the connections, wi. The
sum of the inputs are presented to a function at the nodes. A range of functions can be
used at these decision making nodes provided that they are non-linear and that their input
range is dl real numben. The step function was used in early classification neural
network models [78]. A rarnp function has also been used and has the desirable propeny
of king differentiable enables its use in the backpropagation training algorithm. The
most popular function used in neural networks is the sigmoid function. Sigrnoid functions
are non-linear. continuous, differentiable and asymptotic near the saturation values
(Figure 3.3) and can be defined as:
where: net = x=, yx,
and a s , b=1
Figure 3.3 - A sigmoid function [78]
Other sigmoid functions such as the hyperbolic tangent and arctangent can also be
used. In general. no rnatter what shape of function is selected there is very littie effect on
the capabilities of the network. but it cm have a significant impact on training speed [79].
The output of the sigmoid function is asyrnptotic and is typically limited to values
between O and 1. Although the input range is not lirnited. it can also be normalizcd.
Limits between 0.1 and 0.9 are used for nomaiizing data as it reduces the effect of the
asymptotes. Normalization of ail the data assures that the data all have the same range.
ensuring that no preferences exist.
3.2.1 Training
Since there is no previous knowledge of the relationships of the system king
analyzed. the network must be rrained to interpret the data properly. Learning algorithms
are used during training to modify the weights until the system gives the desired results
within error. The error of a system is determined by comparing the difference between
the predicted output of the network and the output used for training. The most common
training algorithms use a bachard propagation of the total error to caiculate the error of
the previous layers and nodes. Feedfowani networks that used this training method to
change the weights are sometimes mistakenly called backpmpagation networks, which
indicates its popularity as a training method.
Once the error at each node has been calculated, a decision must be made in how
to adjust the weight such that the overall error is minirnized. The simplest
backpropagation algorithms change one weight at a time, then recalculate the network to
ensure that the total enor was reduced and if not. change the weight in the opposite
direction. This iterative process is time consuming. The grdent descent method
improves on the basic algorithm by changing the weight in a direction such that the error
is reduced. If the error function is visualized as a 2-D contour plot, a half ellipse rotated
through 180 degrees in 3-D, the calculated direction to rninimize the error would be
perpendicular to the contour line facing inwards (Figure 3.4).
Figure 3.4 - Gradient descent on a 2-D contour plot of an error function
The amount that a weight is allowed to change per iteration is controlled by the
leaming nie. If the learning rate is too large. the new value calcuiated may have
overshoot the minimum in the chosen direction, which typicall y results in oscillations
(Figure 3.5). A decreasing leaming rate can be used to reduce the arnount of change as
the ovenll error is lessened to reduce the number of iterations required for an acceptable
solution.
. .
Figure 3 5 - Oscillations with gradient descent on a 2-D contour plot of an enor function
In order to avoid oscillations in minimizing an e m r function, a momentwn term
can be added to incorporate a percentage of the previous direction (Figure 3.6). This is
particularly useful for data with sharp peaks or valleys.
Figure 3.6 - Gradient descent on a 2-D contour plot of an enor function with a momentum tem
Roper selection of appropriate leaming rates and momentum tems is required to
achieve convergence in a reasonable amount of time. A conjugate gradient method is
typically used in neurai network training as it adjusts the learning rates and momentum
terms used. The rates and terms are calculated such that a function denved from the
backpropagated error is minimized. thus dramatically reducing the number of iterations
required for convergence when compared to the gradient descent method.
The conjugate gradient method converges rapidly to a minimum, but there is no
guarantee that the global minimum is reached. Depending on the set of weights selected
before training, it is possible that convergence towards a local minimum could occur
(Figure 3.7).
Figure 3.7 - Typical cross section of network error function (791
Cornparhg repeated trials using weights initialized with random values is the
simplest approach to avoiâing local minimums. A better method is simulated annealhg
which varies weights randomly in order to find the global minimum. Annealing is
analogous to its application in metallurgy, in which the molecules (weights) are initially
at an elevated temperature (large deviations in the random numben). If the material was
suddenly cooled. it would quite likely be very brittle (local minimum). if the temperature
was decreased slowly (number of itentions), the amount of movement in the solution is
gently reduced, which would allow the molecules to arrange themselves in a stable
pattern (global minimum). The advantage of this method is that it allows the network to
"jump" out of local minimums without having io repeat the entire training process.
3.2.2 Validation
In order to measure the success of the training, the generalization ability of the
network is tested on previously unseen data. Generalization capabilities can be viewed as
the ability to correctly predict outcornes from data within the range of the training set. but
not included in the training set, cailed a test set. A method used to create a test set is to
generate a training set from experimental data then randomize the order of the training
patterns and finally remove and Save a percentage (10 to 25% depending on the total
number of pattems) in a separate file. A network is said to have good generalization
capabilities if similar error is achieved with the training and test sets. If there is a small
error on the training set but a large error calculated for the test set, then it is possible that
the network has leamed specific pattems and not the overall trends. This is likely to occur
if too many hidden nodes are present or if the network has been overtrained.
To ensure that overtnining does not occur a training method which continuously
compares the training ermr to the test error is commonly used. The training and test error
are either continuously calculated &ter each successive training epoch or the test error
cm be calculated at predeiennined error increments. The weights associated with eûch
test error calculation are also stored. Using this method two distinct trends cm be seen for
the training and test error (Figure 3.8).
r
Training Epochs
Figure 3.8 -Training and test error as a function of oaining iterations
The training error can be secn to decrease as more training iterations are
performed. The test error initially can be seen to decrease until a minimum value is
reached than the error increases. Training iterations beyond the test error minimum
reduced the generalization ability of the network as can be seen by the increasing error
for the test sets. The weights associated with the minimum error for the test sets should
be used for cornparison of different network architectures.
3.3 Categories of Neural Networks
The brief introduction to neural networks presented earlier in this chapter cm be
applied to two distinct categories of neuni networks: static and dynamic. The category in
which a network belongs to is determined by what occun within the neural network
during its operation.
In r static network, the weights and connections are established during training
prior to operation and do not Vary. The quaiity and variety of data included in the training
set is a very important factor in detemining how well the network will perfom. Static
networks are particularly useful for pattern recognition tasks, such as character
recognition. However. it is almost impossible for a static network to associate patterns
wiihout any characteristics that were present in its training set.
Unlike a static network, a dynamic network receives no training pnor to its
operation. Without prior training, the network is capable of adjusting its weights and
connections to match a particular data set. These networks do not require training sets or
time consurning training of the static network. The ability to change makes dynamic
networks more suitable to l e m changing data and be able to forecast funire outcomes.
It is possible to combine networks from these two categories in order to have a
more capable neural network. In this type of network, it would be possible to reduce the
size of and the amount of time require to learn a particular training set. Changes in the
system would be possible without having to retrain the neural network used. A neural
network cornbining static and dynamic neural networks could be called adaptive. Cûreful
interpretation of the previous term must be used as networks which change the number of
nodes or eliminate connections during training have also been called adaptive [78].
3.4 Relating Weld Geometry to Signal Data
A neural network was selected as a better approach to relate the geometric
properties of the weld to the frequency spectmm of the photodiode signals as it requires
no knowiedge of any goveming equations. A computer prognm from Practical Neural
Network Recipes in C t c [79] was selected to creaie a multi-layer feedforward network.
This was used to map the input to the output. Training of the network was accomplished
using the conjugate gradient method and simulated annealing to escape from local
minima. A percentage of the data to be used for verification of the network was not
included in the training set. The data from the three senson and the laser parameters that
could be adjusted were selected as inputs to the network. Based on discussions with ATC
and DaimlerChrysler, Geomevic properties of the fusion zone were identified as
important quality measures were, therefore, selected as the outputs to the neural network.
The neural network software chosen for the analysis used a sigmoid function with an
output range between O and 1. Normaiization of the laser and geometric parameters and
spectrum analysis coefficients between values of 0.1 and 0.9 wen used for training, as
discussed in Section 3.3.
Actual maximum and minimum values, V,, for the laser and geometric coefficients were
used in Equation 3.3. The values for the specmm analysis coefficients were rounded off
to the nearest half increment. The actual upper and lower limits were not used such that
the network would be capable of handing data from additional test without requinng
retraining (Table 3.1).
Upper and lower limits of spectrum analysis coefficients .
Tabk 3.1 - Exrmple of limits used for selected spectrum analysis coefficients
Upper and lower limits used for nomialization
t t t H,dd@n-rn 0 ("-J 0
t t t 00000
0.491 306 -0.591 33-
max min
max 1 2 rn in O
Figure 3.9 - Pyram~d mle for selecting number of hidden neurons in a three-layer network [79]
1 .na31 0.479779
0.976925. -1.30741
1.777299 0.492091
t -1
Lacking specific rules goveming the number of hidden nodes required to perform
and analysis, the geometric pyramid rule was used as a guideline for selecting t!e initial
2 O
1 -2
0.91 2723 -0.63247
0.612695 -1.1 4274
1 - 1
t -1.5
number of hidden neurons [79]. The rule suggests that the number of neurons in a
network should decrease from input to output. Thetefore. if we have n input neurons and
m output neurons in a three-layer network (NHID3), we should have the square rmt of
(mn) neurons (Figure 3.9). Using the same nile, the suggested number of neurons for a
four-layer network (NHIDd) are:
Applying the above mie, initial network configurations of 19-12-8 and 19- 14- 1 1 -
8 were used. The number of hidden nodes was increased and decreased and training was
performed until the error was no longer reduced after repeated training. Alter training, the
network was presented with the data left out of the training set and the results were
compared to those achieved dunng training.
Chapter 4 Experimental Procedure
4.1 Introduction
The experimental mode1 described in Chapter 3 requires a set of experimentd
data to be gathered. A production laser welding system used for welding cylindrical
transmission gean was used for this purpose. The machine was equipped with senson
and data acquisition system which was used to gather the required data. The machine
specifications and the experimentd set-up are discussed in this chapter.
4.2 Laser Welding System
An indusvial partner with an operational laser welding system was selected for
the experiments. ATC Powerlasers of Kitchener. Ontario was building seven gear
welding machines for DaimlerChrysler in Kokomo, Indiana during the surnmer of 2000.
These machines were equipped with an operational monitoring system consisting of three
photodiode senson and data acquisition boards. All seven systems were designed to weld
cylindrical pans with a CO1 laser. Six of the machines had been designed for full
peneuation welds. while the seventh machine was going to be used for a blind weld. One
of the seven machines was used for al1 of the experiments perfomed in this study (Figure
4.1). ATC donated time and technical support with the equipmeot and DaimlerChrysler
supplied d l the test parts.
Figure 4.1 - Laser welding ce11 by ATC Powerlasers for DaimierChrysler
The welding machine incorporates an 8kW COt RF excited laser from TRUMPF
Inc. with helium shielding gas. TRUMPF Inc. also supplied the two-axis positioning
system for the welder head. In order to maximize the on-time of the laser unit. the
welding head is moved between two rotating spindles that are alternatively used. The
position of the laser head c m be adjusted through the machine's control panel. but
remains fixed at each spindle during the welding sequence. Automation is used
throughout the machine to locate and transfer the parts.
Figure 4.2 - Gear components: shah & cup (a). welded subassembly (b)
The machine used for experimental runs welds a cup with five tabs to a flat plate
in order to form a partial input carrier assembly for an automatic transmission (Figure
4.1). The cup is cast from mild steel (MS-SAE 1010) and the plate is made from sheets of
hi@-suength, low-alloy steel (HSLA SAE J1392). No pst-weld heat treatment is
performed. The full Iength of the five tabs (34mrn) is welded in a specific sequence to
avoid distonion in the part (Figure 4.3).
Figure 4.3 - Weld sequence on an un-welded piate
The plate is press fit on the cup using an air-over-oil hydraulic press, which gently
ben& the tabs inward before forcing the disk down. Bending the tabs ensures proper
alignment and reduces the press force requi~d to join the parts.
1
1
I
t I
i
!
Weld direction
Figure 4.4 - Shield gas nozzle location
Heliurn shield gas is supplied through a nozzle that is located just above the
surface of the plate in front of the weld (Figure 4.4). The fiow of helium gas sians a few
seconds before welding and remains on in between welding of individual tabs to ensure
proper shielding. The noule is directed to blow almost horizontally dong the weld line. \
Fingers index beside the tabs to maintain the proper offset distance from the surface of
the cup. Servomotors are used to control the rotational velocity of the spindles and the
angular position was used to trigger the laser welder. Cycle time is approximately 25
seconds. dependent on the rotational velocity of the spindle.
The size and shape of the weld depends upon the types of materials being welded
and the configuration of the laser welding system. In sheet metal, the geometry
(concavity, convexity) is a gaod indication of the total area of the weld making it an
57
important measure of quality for tailored blanks [81]. Penetration depth and complete
fusion without porosity are quality measures for deep welds used for gear welding.
1 Parameter 1 Variable 1 Laser power 1 % of total 1 Cornputer - I 1 Openting mode 1 Pulsed or Continuous 1 Cornputer - I
Frequenc y Weld speed Focus height Lateral focus location Beam mgle relative to part nomai Shield gas flow rate
Table 4.1 - Typical panmeters in a rotary laser welder
Shield gas nozzle location Shield gas composition
Table 4.1 lists 10 different panmeters that c m affect weld quaiity in the welding
Hz 1 Cornputer
of rotary gears. Note that pararneters 1-7 are under computer control and pararneters 8- 1 1
deg Jsec mm mm dea. Umin mm He, Ar, CO2
are under manual control. Computer controlled parameters are easily varied by a trained
Cornputer ,
Computer Cornputer Manual Manual
\
Manuai rn
Manuai
opentor on the control panel. Many parameters are manually set and may require the
system to be shut down in order to perform modifications to the unit. Once determined
through prototyping and testing, the value of the individual paameters rarely changes.
4.2.1 Monitoring System
The Weld Process Monitor (WPM) is a qudity monitoring system that uses a
scraper mirror (Figure 2.12) with fibre optic output to direct light emitted from the
keyhole to three photodiodes and is incorporated into the laser optics. The output of the
fibre optic cable is divided into three photodiodes sensitive in the UV, IR and visible
regions of the optical spectrum. The particular response curves of the individual
photodiodes are show below (Figure 4.5).
1 O0 600 1100 1600
Wmvahngth (nm)
Figure 4.5 - Responses curve of the three photodiodes of the ATC WPM system [82]
A CO-axial mounting position was used as it was a simple addition to the standard
optics and did not affect the incident laser power or require exua space. An added benefit
was that the site, which is monitored, remained at the same Iocation relative to the weld
spot for both spindles.
A data acquisition board controlled by a LabWindows program was used to
acquire the data from the three photodiodes at a sarnpling frequency of 900Hz. Past
experiments performed at ATC Powerlasen indicated that the sampling frequency of the
system was adequate for use in quaiity monitoring. The output signal of the photodiodes
w u amplified such that the full range of the data acquisition system could be used over a
variety of welding conditions. A single text file was generated by the program that
contained four columns of information (W. IR, visible, time) using the date, start time
and spindle number for the filename (Appendix A). The WPM is a stand alone IBM
compatible cornputer with a touch sensitive screen (A), touchpad (B), keypad (C) and
59
LED sensor signal output @) with a communication link to the main controller of the
welding ce11 (Figure 4.6 and Figure 4.7). The WPM receives a signal when welding
occun and sends a signal when the part is deemed to have failed the quality requirements.
The system has shown promise in identifying a bad put but it relies on an experienced
user to continuously monitor and maintain its settings. It has shown the ability to detect
the lack of penetration by a sharp rise in signal levels. However, the WPM is incapable of
detennining internai type or the degree of defect preseni. Limitations in signal processing
methods, and not in sensor technology, were cited as problems with the existing system.
Figure 4.6 - Weld Rocess Monitor from ATC Powerlasers [82]
0 Lateral focus location: Was increased and decreased in lrnm increments until the
joint was visible (Figure 4.8, Figure 4.9).
Figure 48 - Weld on tab only (a) weld on disk only (b)
Figure 4.9 - Proper lateral location for weld
Focus height: Was increased in 1 mm increments away frorn the part only until partid
penemtion occurred. Focus height was not decreased as the shield gas nozzle would
have been damaged.
Weld speed: Was increased and decreased in 5 degredsec intervals. The minimum
rotational speed w u determined by the cycle tirne requirements and excessive
material ejection. The maximum speed was determined when partial penetration
occurred on the undetside of the joint (Figure 4.10).
Figure 4.10 - Full penemtion (a) and partial peneüation (b) as viewed from the undeaide of the
joint.
Forty-nine welded components were made using different combinations of lateral
focuses. focus heights and weld speeds . These panmeters (i.e.: the laterd focus. focus
height and weld speed) were. therefore, selected as inputs of the neural network.
Although the laser power was not varied dunng the initial variation it was included as
one of the four welding parameters used as inputs to the neural network. A complete
listing of al1 the variations and the associated part numben are included in Appendix B.
Simulated production runs at standard settings were executed on both spindles as part of
the machine's buy-off procedure.
Records of visual inspections of the top and bottom surfaces of the 132 parts (68
Spindle 1. 64 Spindle 2) welded during these runs contained data on surface
imperfections (pinholes, concavity, convexity) and the presence of partial peneuation
(Figure 4.11-Figure 4.14) and cari be found in Appendix C.
(a) (b)
Fipre 4.1 1 - Close-up of top surface: good (a) pinholes (b)
(a) (b)
Fipre 4.12 - Close up of top surface imperfections: concavity (a) and convexity (b)
(a) (b)
F i p r e 4.13 - Close-up of bottom surface: good (a) and pinholes (b)
Figure 4.14 - Close-up of bottom surface imperfections: partial penetration (a) and excessive
material ejection (b)
4.4 Sample Preparation
Metallographic sectioning of the welds was performed on 83 of 182 parts to
detemine the quaiiiy of the welds. DairnlerChrysler retained the remaining 99 parts (5 1
Spindle 1, 48 Spindle 2) as required by their machine buy-off procedure. A horizontal
band saw was used to separate the plate with welded tabs from the remaining cup and
shaft material. Each tab was scnbed with its number to ensure accurate tracking (Figure
4.15a). A section of Smm in thickness was cut from each tab using a wet-saw
approximately lOmm from the start of each weld (Figure 4.15b).
Figure 4.15 - Scnbing the tab number (a). wet-saw used for sectioning (b)
The sections were mounted in epoxy pucks of 40mm diameter dong with an
embedded piece of paper containing the tab locations and part number (Figure 4.16). The
sarnples were polished with successively finer gits of silicon carbide sandpaper (120.
220,320,400.600) before a final 6p.m ddiamond polish.
(a) (b)
Figure 4.16 - Sections mounted in epoxy puck (a) information tag (b)
The samples were then etched in a 10% Nital solution in order to reveal the
material microstmcture and the weld fusion zone. An Olympus S240 stereo zoom
microscope. equipped with a ring light for illumination and digital carnera, was used to
inspect the cross-sections (Figure 4.17a). A Nikon Coolpix 990 colour CCD digital
camen was used to capture al1 the images (Figure 4.17b). The camera, capable of
capturing high-resolution images (2048X1536 pixels). was fitted to the microscope with
an adapter in the eyepiece.
Figure 4.17 -Stereo zoom microscope with ring light (a) image from digital camen (b)
4.4.1 Image Analysis
Image-Proo Plus was the image analysis software selected for al1 geometric
analysis of the weld fusion zone [83]. This software was king used by ATC Powerlasers
and is a popular choice in industry and academia. The variable zoom settings on the
microscope and the camera were fixed and the camera was set on manual focus to ensure
constant overall magnification of the samples. Images of a scale were also taken as a
reference for the magnification.
The first step of the anaiysis was to rotate the image such that the top edge of the
plate w u aligned with the horizontal. The overall weld fusion zone, not including the
heat-affected zone. but including holes, was traced with the outline tool (Figure 4.18).
The number and area of holes in the fusion zone were counted separately when they
Figure 4.18 - Weld area rneasurement with the outline tool in tmagePro0 Plus
The thickness of the plate (T3) was measured between two lines rnarking the top
(Ll) and bottorn (L2) edge of the plate (Figure 4.19).
Figure 4.19 - Thickness masurement in hgeProQ Plus
The thickness of the plate was found to Vary within the manufacturer's
specification (4.32mm to 4.58mm. therefore. it could not be used to verify the image
calibration. The coordinates of the outer corner of the plate were also measured as they
can be used as a reference for any positional calculations (Figure 4.20).
Figure 4.20 - Single point meastuement in 1magePro0 Plus
There are 29 different measurements that the software c m perfonn on the outlined
weld fusion zone. Since this was an automatic calculation it was decided to record 14
different geometric propenies with the option to ignore particular measurements later in
the analysis (Figure 4.2 1).
Figure 4.21 - Geometnc proprties of the weld area [83]
The nurnber and a m of any holes in the weld area were recorded (Figure 4.22).
The area of the holes was combined and converted to a ratio of the total weld m a . A
spreadsheet was created that coniained the weld properties. hole properties. disk
thickness and corner coordinates measured for each tab.
Figure 4.22 - Hole measurement in lnugeProO Plus
The geometric panmeters from the fusion zone selecied as potential quality
measures were: weld area, aspect ratio. X coordinate of the centroid of the weld area
measured from the outside corner of the plate, length of the major axis, length of the
rninor mis, disk thickness, number of holes and hole area ratio. The neural network used
these eight parameters as the outputs.
4.5 Data Preparation
The individual text files generated by the WPM system were divided such that the
new files contained information for a single tab only. The separation of the data into the
individual tabs was accomplished by searching for a series of zeros in the signal output
column and a larger time interval between sarnple points that were Witten by the WPM
systern (Figure 4.23, Figure 4.24).
Figure 1.23 - Complete data file: 06-26- 16- 10- 16-S 1
- Visible
Figure 4.24 - Data file for a single cab: 06-26-16-10- 16-S 1-Tl
Investigation of the time intervals between datapoints during welding revealed
occasionai gaps due to the Windows-based operating system. The placement of the gaps
was random in nature and the frequency of the gaps increased when the ce11 was
continuously running in production mode with both spindles operational. Linear
73
interpolation between the data points on either side of the gap was used to mate a file
with constant time intervals (Figure 4.25). The value of the interpolation was rounded to
the nearest multiple of four to be consistent with the original sampled data.
Figure 4-25 - Gnph of original and interpolated data for a single sensor
A time iag was found to exist between the start of signal acquisition and the start
of welding. A second program was therefore written that eliminated starting data points
until a preset percentage of the maximum value of one of the three senson was reached
(Fi gure 1.26).
Figure 4.26 - Graph of original and interpolated data at 5% of maximum signal value
4.5.1 Spectrum Analysis
The data was converted from the time domain to the frequency domain using
Maximum Enuopy Method (MEM). MEM (also known as an dl-poles model) is an
alternative to Fast Fourier Transform m) analysis. The advantages of this system are
that i t can be quicker to mn than FFT and it has the ability to fit sharp spectral peaks [84].
The number of coefficients selected determines the order or number of poles in the
approximation. The number of poles used in an approximation determines the mount of
features ihat can be identified (Figure 4.17). .4 smaller number of poles requires less
analysis time and creates a smoother output specmm. If tw rnany poles are selected than
this method may show phantom peaks when compared to anal ysis.
. l S .- 7
frequency f
Figure 13.7.1. S;implc ourput of maximum cnaopy specrml esumtion. Thc input signai consists of 5 17 sarnpies of the sum of two sinusoids of vcry ncarly the u m c fkquency, plus white noise with about c q u d powcr. Shown IS an rxpandcd parnon of thc full Nyquist frcqucncy interval (which would cxtcnd from zero IO 0.5). The dashed spccml esumatc uses 20 polcs; the doncd. M: the solid. 150 With the I q c r numbcr of ples. the mcthod can rrsolve the dininct sinusoids; but the h t noise background is b e g i ~ i n g to show spunous peaks. (Note logmrhmic scale.)
Fipre 4.27 -Approximations of a sinusoidal function using the MEM with different number of
A Computer program incorporating code from Numerical Recipes in C [84] wiis
wntten to perform the analysis on al1 the sarnple data. In the current research 5 p l e s
were selected as any more led to the creation of phantom peaks.
Chapter 5 Experimental Results
5.1 Introduction
The results of training and testing numerous neural networks to associate t h e
weld pool parmeten to the shape and relative position of the Fusion in gear welding is
presented in this chapter. The analysis of the geometncal properties used to determine
the quality of the weld is also discussrd in this chapter.
5.2 Geometrical Properties of the Fusion Zone
The image analysis software, Imagepro, is an excellent 1001 as it allows for
automatic calculation of the geometrical properties of the fusion zone (Figure 4.31). Four
measurements were performed on the weld cross-sections in order to generate the set of
propenies chosen to describe the fusion zone: presence of holes. area of holes. thickness
of the disk. md a single point at the outer corner of the disk that is used to detemine the
lateral position of the fusion zone. Precision of these measurements was determined by
performing repeated runs on a *mup of nndody selected sarnples. Studeni's t-
distribution was used as it is applicable for small sarnple populations.
5.2.1 Area of the Fusion Zone
The weld m a including al1 holes and excluding the heat affected zone ( H M ) was
measured. The analysis software had the ability to automatically trace an area based on
brightness, contmt and colour differences in the image. Unfortunately this feature was
only useful dong the top and bottom outer edges of the weld and required manual
intervention in order to accurately follow the edge of the actual fusion area. Repeated
rneasurements were performed on selected samples that indicated an error estimate of
2%.
5.2.2 Thickness of the Disk
Thickness measurements were performed by dnwing lines on the top and bottom
surfaces of the disk. The tolennce on the thickness of the disk resulted in the top and
bottom surfaces not always being parallel. The average thickness of the disk over the
length of the two lines was used for analysis and estimated to Vary by +/- 0.07mm. Al1
samples were found to be within the manufacturer's specified thickness of 4.45 +/-
O. 15mm.
5.2.3 Area of the Holes
Unlike the total weld area, it was possible to use the automatic tmcing feature in
the image analysis software to measure the area of the holes. This was possible as the
holes appear darker in the digital images. The automatic hole measurement reduced
possible human enors but. due to the small overall area of most holes. a precision error of
5% is estirnated. A C++ program was written to count the number of holes and to
calculate a ratio of the total area of holes to the totai weld area including holes to reduce
the absolute error.
5.3 Neural Network Architecture
As described in Chapter 3, a multilayer feedforward neural network is considered
for the task of modeling the relation between sensor signds and geometrical properties of
the fusion zone. The number of inputs. outputs and hidden nodes describes the
architecture of such a network. The input and output node counts are detemined by the
mode1 requirements while the number of hidden layen and nodes in each layer are
determined by model performance. The mode1 performance is also affected by the design
of the training sets, data preparation and presentation to the network.
Unfortunately. only genenl guidelines exist for how to design a neural network or
the training patterns. In order to achieve the best possible results. different ways of
presenting the experirnental data to the network and architecture of the neural network
itself had to be exarnined.
5.3.1 Evaluating Performance of the Neural Moâel
The performance of the neud model is evaluated using root mean square (RMS)
emor. Root mean square e m r has a few desirable properties. which have made it the
method of choice for evaluating the performance of neural network models. These
include the ease of calculation. emphasizing the large emn and the ease with which the
denvative of the emor can be computed for optimization purposes [79].
The RMS e m r for an individual output is defined as.
where. t is the target value for the output a is the actual value for the output r is the number of samples
Each neural model is evaiuated twice: once against the training set consisting of
patterns with which the neural network was trained and once with a test set. The test set
consists of data set aside for evaluating the generalization capability of the neural model.
The neural network model has not seen these pattems during training.
In each case, the total error for the network is calculated by averaging the
individual RMS errors over the entire set. i.e.,
where. s is the number of output variables in the network.
Initial tnining was performed by repeatedly presenting the training set to the
network until a minimum training error was reached. Once a minimum training error was
reached the network was presented with a test set. This method creates the risk of
ovemaining the network. however. and indication of the upper bound on the test error
and a lower bound on the training error cm be found. Once a reasonable upper bound on
test emr has been found, the optimal training will be attempted by comparing the test
and training enors as outline in Section 3.2.2.
Based on discussion with ATC and DairnlerChrysler a target of less than 10%
error on the individual output parameters was deemed to be acceptable.
5.3.2 Training using Sequential Data
As previously mentioned, five tabs are welâed on each gear. The initial training
set, therefore. consisted of the data from the fint four of the five tabs from each gear. The
data from the remaining tabs was used for the test set. The training set was presented in
chronologicai order to the neural network. Figure 5.1 shows the total error for the
network when trained with sequential data. The training error reduces as the number of
hidden nodes increases. The test error follows a general trend of increasing with the
addition of hidden nodes. This trend is not entirely consistent as the test error for 12
hidden nodes is srniiller than the networks trained for 10 or 8 nodes. The decreasing total
training error and increasing total test error with the addition of hidden nodes indicate
that the network was overtrained. Even though the network was overtrained the values
can be interpreted as an upper bound on the test error and a lower bound on the training
error. Similar behaviour is seen when the individuai results are presented (Figure 5.2.
Figure 5.3). The area of the fusion zone and the lateral position of the fusion zone
typically have the lowest individual errors whereas the thickness of the disk and the
number of holes present in the fusion zone have the highest individual errors.
Figure 5.1- Sequential training set (total enor)
Number of hldôan no&$
Figure 5.2 - Sequential training set (individual training enor)
=5 O
t8 16 14 12 IO 8 6 4 2 1
Numbef of hidden nodes
4- x-pos
+axis major '
+ a s minor + thickness
I - lholes - hole ratio
Figure 5.3 - Sequential training set (individual test error)
5.3.3 Training by Randomizing the Data
A second attempt at training w;is undertaken by randomizing the order of the data
present in the training set. A test set was fonned by removing fifty patterns frorn the
data. Minor decreases in the total training error and small increases in the test error were
found in cornparison to the sequential training set (Figure 5.4). The difference between
the total test and training error and the increasing total test error with an increase in the
number of hidden nodes indicate that overtraining occurred. The individual training error
of the fusion zone was found to have decreased (Figure 5.5). It is interesting to note that
the error fluctuation as a function of the number of hidden nodes, is reduced as a result of
this randomization (Figure 5.6).
Fipre 5.4- Results for randornized training set (total error)
1 -a- area i aspect
' x-pos f - 1
I -a+ a#s major '
/ +a#s minor i
1 +hid<ness ' l
1 -#holes ~
Figure 5.5 - Results for randomized training set (individual training error)
2 - hole ratio : 0 -
18 16 14 12 10 8 6 4 2 1
Himbsr d h W n -8
a r e a 1 ! t
aspecï i /
x-pos ! ' 1 .+sas major 1 ,
+a#$ minor 1 1
+ ttiickness / I i !
15; 1 -#holes
- hole ratio :
O 18 16 14 12 10 8 6 4 2 1
Numôar al hidckn nackr
Figure 5.6 - Results for randomized uaining set (individual test error)
5.3.4 Input Normalization
Since outputs of sigmoid functions used in the neural network are limited to
values between [O, 11, the outputs have k e n nomalized to the range of [0.1.0.9] as
previousl y discussed in C hapter 3. Theoreticdl y. nonnaiizing the inputs is not required.
but previous experience has shown that such nomalization may improve the performance
of the network [76]. Training was repeated with ali the input and output values
normalized between O. 1 and 0.9.
Improvements were observed in leaming the uaining set while no noticeable
change in the performance of the test set was evident (Figure 5.7). Increasing total test
erron with the addition of hidden nodes and the difference between total test and training
errors indicate that ovenraining has occurred. The error in several of the individual
training panmeten. including the area of the fusion zone and lateral position of the
fusion zone, appear to converge toward a minimum of 4% and 5% respectively when 8 or
more hidden nodes are used (Figure 5.8). Irnpmvements of the test error for the fusion
zone area and its lateral position are achieved, both under 10%. with the exception of 16
hidden nodes (Figure 5.9).
pq . test
Figure 5.7- Fully nortnalized training set (total error)
i t a r a , I
! apect ! - x - r n Il l-rcaps major i / os minarll
/ ! c t t i i c b a i l
;-#i~ler jl
Figure 5.8 - Fully nomdized training set (individual training error)
Figure 5.9 - Fully n o m l i z e d training set (individual test error)
5.3.5 Two Hidden Layer Neural Network
Although one hidden layer is sufficient for a neural network to l e m any input-
output relation. a second hidden layer cm sometimes improve training results.
Unfonunately, when an additional hidden layer is added, there is an increase in the
amount of time required for training results. More than two hidden layers are seldom
used as this does not. in general, improve the performance of a neural network.
Analysis was perfonned using the fully nomalized training set described in
Section 5.3.4 on a small selection of possible two hidden layer architectures. The total
training error of al1 of the two hidden layer networks were comparable to the erron for
the networks containing ten or more nodes on one hidden layer. The best total test error
achieved using two hidden layers, 12%, was comparable to the second worst total error
using the single hidden layer fully normalized network (Figure 5.10). Overtraining was
dso found to be present with two hidden layea as there was a significant difference
between the total test and training emr. Cornparisons of the individual erroa were
87
similar in nature but varied in magnitude (Figure 5.1 1, Figure 5.12). Therefore. adding a
second hidden layer does not appear to improve the performance of the network.
- - I 14 l
A
, 8 12 1
V
s 10
5 8 1. test 1 6
4 ! 2
O i
Figure 5.10- Two hidden layers (total error)
12 t -a- area I
A 10 a s w , 8 - XQOS 1 Y
8 t
j ++ a#s major 1 + a i s minor
S ~ t h i c k n e s s ! a 4 1
I
Numkr of hiddan nodm
Figure 5.1 1 - Two hidden layers (individual training error)
Figure 5.12- Two hidden layen (individual test error)
5.3.6 Elirnination of Samples Containing Porosity
As previously rnentioned, the main objective of this work is to estimate the shape
and relative location of the fusion zone. Inspection of the individual errors revealed that
samples containing porosity were a major contributor to the total error. As rwt mean
square error emphasizes the large erron, this effect would dominate the network output.
In an attempt to further improve the performance of the neural network in estimating the
area of the fusion zone and its lateral position. it was decided to remove samples
containing porosity. In addition, the two parameters associated with porosi ty. number of
holes and porosity ratio. were elirninated from the training and test sets. From this point
fonvard the training sets created by removing porous sarnples will be refened as the PSE
(porous sarnples excluded), and the training set including porous sarnples will be refemd
as the PSI (porous sarnples included). Note that boih of these data sets are normalized as
described in Section 5.3.4.
5.3.7 Improving Generalization by Adding Noise
Due to the large number of parameters, neural networks are very sensitive to
overfitting. i.e.. they will leam specific pattern characteristics in the training set at the
expense of general input-output relations. One way to reduce the ovenitting problem is to
use a larger training set. It is important to address this problem since. by eliminating the
sample containing porosity. the training set has become smaller.
Genention of training pattems is often expensive andor time consurning. as is the
case in this work. Other methods of expanding the training set must, therefore, be
explored. One such method is to generate additional training cases by superimposing
random noise on a measured set. This may improve the ability of the trained neural
network to handle noisy data that will be presented to it later and will also reduce the
likelihood of overfitting [85]. When additional data is generated. careful attention must
be paid to ensure that data created from the same original pattems is not present in both
the training and test sets. Keeping data generated from the same original pattern separate
ensured that a pmper test for generaiization occurred. The precision error in measuring a
parameter was used to determine the amount of noise added to the data.
A ciramatic decrease in the total training error was found with the implementation
of the aforernentioned changes to the PSE. achieving a minimum near 3 1 (Figure 5.13).
The total test error, however, is sirnilar to the PSI. No additional data was added to the
PSI set. Similar to the previous network configurations the difference between the total
training and test enors indicate that overtraining occurred.
Figure 5.13- PSE (total error)
The PSE total tnining error showed significant decreases over the PSI error
(Figure 5.14). The decrease in the total training error with an increase in the number of
hidden ndes was common between the two different networks. However, similar
decreases were not found with the total error in the test sets (Figure 5.15). The total test
error is similar in magnitude regardless of how many hidden nodes were used.
Figure 5.14 - Comparison between PSI and PSE (total training enor)
Fipre 5.15- Comparison beiween PSI and PSE (total test error)
5.3.8 Optimizing ktwork Training
Due to the significant decrease in training error achieved with the PSE data and
to ensure that ovenraining does not occur training was repeated to include evaluation of
the test error at several steps. Thirteen incremenis in training error mging from 15% to
O. 1% were selected to stop training and evaluate the test error for both the PSI and PSE
network. For a given number of hidden nodes the total test and training error cm be seen
to decrease with increased training epochs. For the PSI network the total test and training
error begin to diverge below the training of 0.75% (Figure 5.16). The PSE network
exhibited a similar difference between the total training and test error but at a lower
training error of 0.5% (Figure 5.17).
a train t e s t
Figure 5.16 - PS l (total error 4-û hidden nodes)
35
8g 20
. H test 1 8 15 = 10
5 O
Figure 5.17 - PSE (totai error 40 hidden nodes)
The uaining of the network was considered optimized and capable of
gnenlization at the point before the test and training error began to diverge. When these
training values are compareci it cm still be seen that the PSE training set resulted in lower
total training and test error (Figure 5.18, Figure 5.19). The lowest total test and training
errors for the PSE sarnples were achieved when 70 or 30 hidden nodes were used.
Figure 5.18 - Comprison ktween PSI and PSE (optimized total training error)
1
, a PSI , PSE
Figure 5.19 - Cornparison between PSI and PSE (optimized test e m )
Of the parameten under investigation, the fusion zone area and its lateral position
are the most important when it cornes to quality control. The fusion zone area and its
latenl position are among the parmeten with the lowest individual training and test
erron (Figure 5.20. Figure 5.2 1). The training erron for the fusion zone area for PSE
samples are typically sevenl percent lower than the PSI samples (Figure 5.22). Training
errors of less than 4% were achieved in the former case. The test emors for the fusion
zone area were half the magnitude of the PSI samples for over half the cases (Figure
5.23). Test errors below 4% were achieved for the PSE samples for several different
hidden nodes combinations used dunng training.
O [ r i 200 180 120 70 60 50 40 30 20 10
Number of hidden nodes
1 aspect i l i I j : - x-pos
! / i * axis major, i 1 1 -+ axis minor i I I
/ - thickness i
Figure 520 - PSE (individual training emr)
I
200 180 120 70 60 50 40 30 20 10
Number of hidden nodes
+ area aspect x- pos I
* axîs major l
-e- axis minor + thickness : i
Figure 521 - PSE (individual test error)
Figure 5.22 - Comparison between PSI and PSE (area training error)
n 12
8 10 - 8 I PSI
L 6 U)
.PSE ' a
2
O 200 180 120 70 60 50 40 30 20 10
Number of hMâen nodes
Figure 5.23 - Comparison between PSI and PSE (area test error)
The training error in the Iateral position of the fusion zone for PSE showed a
small decrease in magnitude over PSI for most variations in the number of hidden nodes
(Figure 5.24). The test e m r on the lateral position of the fusion zone was lower in al1 but
two of the cases. On several occasions the magnitude of the PSE data was almost half the
PSI data (Figure 5.25). Significant improvements to the test error and minor
improvements in the training error were observed when training with PSE data cornpared
to PSI data. Individual training and test errors below 5% for the area of the fusion zone
and its lateral position were achieved for PSE data when 70 hidden nodes were used.
Figure 5.24 - Comparison between PSI and PSE (laterd position training error)
.7
op 12 v L 10 I
8 P S I i
O I
u, 6 P S E ;
B 4 I
2 I
O 1
200 180 120 70 60 50 40 30 20 10
Numbet of hiâden nodes
Figure S.= - Comparison between PSI and PSE (lateral position test error)
The neural network model has been shown to be capable of estimating
geometncai properties of the fusion zone frorn laser-welding sensor data. To ensure the
success of the neural model. careful attention was paid to the manner in which the
training data is presented to the network. Ali the input and output data used to generate
the training set were normalized to an identical range to facilitate training. Adding a
second hidden layer to a network did not improve the performance and, therefore, was
not further pursued. It was dso found that reducing the dernand on the neurd network by
eliminating hard to predict outputs irnproved training. The most significant reduction in
error was achieved by generating supplementary training cases through the addition of
random noise to the measured data set.
Chapter 6 Conclusions
6.1 Contributions
A weld monitoring system using neural networks has k e n developed that can
estimste the cross sectionai area and lateral location of the fusion zone in a laser welded
transmission gear. The estimates are based on measurements from ultra-violet. visible
and infrared photodiodes. It has been shown that through proper signal processing,
judicious choice of architecture and application of appropriate techniques, a neural
network c m be trained to correlate photodiode signais to fusion zone properties with
reasonable accuracy.
6.2 Concluding Remarks
The work described in this thesis was aimed at developing a system for estimating
the shape and location of the fusion zone in a laser gear welding application. In general, a
good weld cm be characterited as having a fusion zone that is as deep as the material is
thick. The fusion zone should have a consistent width from the top to bottom surface of
the joint. Maintaining a consistent depth and width of the fusion zone in a weld results in
a better joint that is able to evenly distribute any loading throughout the entire weld
region.
Existing quality standards recornrnend the practice of destructively testing a
random selection of a representative sample of parts. The best method to determine the
shape and size of a laser weld is to cut a section through the joint and perfonn a
metallographic inspection on the fusion zone.
Due to the Iength of time required to cut. polish and mesure the fusion zone in a
welded transmission gear component. many bad parts may be produced before a problem
is noticed. From an economical standpoint, it is impractical to destruciively inspect al1
parts produced. Therefore, non-destructive inspection techniques must be used, in
addition to periodical destructive tests, in order to determine the quality of the paris
produced.
Neural networks were investigated as a tool for predicting charactenstics of the
fusion zone in a laser weld as part of a quality control system. Using information
gathered from previously welded and inspected components, a training set was created
and used to train a neural network. The main advantage of using neural networks is that
the network iiself determines relationships between the input and output signals. In laser
welding, the relationship between photodiode signals of the rnolten weld pool and the
size a d shape of the fusion zone are not well known due to complex physical
interactions present during the process.
Different methods of generating the training sets and different neural networks
intemal architecture were investigated. 1t was found that improvements in the error of
the training and test sets could be achieved by randornizing the order in which data was
presented to the system and by using fully nomalized data. The improvements were
most noticeable in the test error for individual parameters. Presentation of the data in a
random order removes the possibility of learning any trends that may exists between
groups of welds. This is especially imponant as the data for the training set is generated
by incrementally varying the welding parameters that the network may interpret as king
a requirement for a good weld. Normalizing the input data between the sarne upper and
lower values as the output data ensures that al1 the data is treated equally by the neural
network.
Minor improvements in the training set error were found when a second hidden
layer was added to the neural network. However, the test set error increased and the time
required for training dramatically increased with the addition of a second hidden layer.
This confirmed that a second hidden layer is very rarely required in pattern recognition
networks.
The training set error was typically more than halved when the network was
trained with data from welds that contained no porosity. The mount of data in the
training set was dso increased by adding a perceniage of random noise, based on the
precision error of the measured parameters, to the data used in the previous training sets.
The individual parmeters that were best predicted by the neural network were the
fusion area and the laterai position (x-pos) of the fusion zone. The depth of the fusion
zone (major mis) was also well leamed by the network. When only full penetration
welds are under investigation, the area and the lateral position of the fusion zone are
quality measures that c m be used to determine the quality of a weld.
The work presented has shown that the cross sectional area and the lateral
position of the fusion zone in a laser weld can be predicted with reasonable certainty.
However, other parameten that were investigated such as the number and area of the
holes could not be predicted.
Future work should concentrate on improving the accuracy of the prediction of
the weld ares and its geomeuy, as these mesures can be used as a quality mesure. This
could be accomplished by increasing the number of variations within the training set and
the number of samples used to generate the training set. Methods of increasing the size of
the training set through artificial means. such as adding random noise, should be funher
investigated as it is much easier to genente more samples with a cornputer ihan getting
materiül and tirne to be used on a production machine.
The addition of a dynamic neunl network after the static neural network may also
be able to improve the accuracy of the network. while reducing the arnount of training
required. It may be possible for such a network to predict accurateiy outside of the
training set used for training the static network. The largest benefit of such a system
would be that a network would not have to be retrained after adjustments are made to the
processing parmeten.
Unfonunaiely, the system presented did not work very well when attempting to
identify the area and number of holes present within a sampie. This is most likely due to
the relatively small size of the holes in cornparison to the totd weld area. Instead of
trying to teach a neural network to identify the characteristics of the holes in a given
simple, it should merely be asked to try and determine the presence of pomsity.
Finally. the system must be able to function properly in a production environment
with minimum intervention. In this case, the addition of a second dynamic neural
network to improve the operating range of the system would prove invaluable as it would
reduce the need for retraining after any adjustrnents in the system.
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Appendix A
Portion of sample output file 06-26-16-10-16.Sl.txt
UV 116 116
Time (s) UV 76 16 20 - O
IR Time (s) ' 0.05 0.051
IR 40 48
VIS VIS 4a 56
Spindle 1 standard
Appendix B
setting and tabk of test variations
Spindle 1 standard setting I
Laser Power 1 94% of 8kW I Pulse freauencv 1 20 OOOHz I
06-26- 1 3-27-29-SI standard settings 06-26-1 3-28-32-SI standard settings 06-26- 1 3-33-32-SI x-axis towards tab 1.264 06-26- 1 3-36-08-SI x-axis 1.284 ,
û6-26-13-38-21 -SI x-axis away from tab 1 224 û6-26- 13-41 -1 O-SI x-axis 1.204 06-26-1 3-45-1 6-SI z-ais up to 9.27 and x-axis 1.204 06-26- 13-464-SI z-axis 9.27 1 mm up and x-axis to 1.244 , 06-26- 1 3-48-47-SI z-axis 9.23 2mm up
Filename 06-26-09-06-1 1 -SI _ 06-26-09-1 6-54-SI 06-2669-36-5 1 -SI 06-26-1 3-1 9-1 1 -SI 06-26- 1 3-20-03-SI 06-26- 1 3-21 -05-SI 06-26-13-22-21 -SI û6-26- 1 3-23-1 241 06-26- 1 3-24-04-SI 06-26- f 3-24-55-SI 06-26- 1 3-26-41 -SI
- - - -
106-26-1 4-50-34-SI [z-axis 9.27 1 mm up and speeâ 65 1
Variations .bad noule location bad nonle location standard settings standard settings standard settings standard settings standard settings standard settings standard settings standard settings standard settings
,06-26-14-53-51 -SI 06-26-1 4-5743-SI 06-26-1 4-59-23-SI
-2-axis 9.27 1 mm upand speed 55 z-axis 9.31 (initial), speed 55 and x-axis 1.264 soeed 65 and x-axis 1 -264
Appendix C
Record of Visual Inspections petformeà of parts betore sectioning
Good Sauirt 5 at end
wl pinhde 5-5 Pi- 1-21,29, 4-1 2, CmW 5-30, biq pi- enci 5-31 Pt- 1-26.3-23, 24,25,26.2?. 29, ri, 31,31.5, 32, 33, 51.5, 18.33, nreak periebam 2-1 3 OK cram end 5-31 Pinhdes 1-17,20,23 3-26.44, end 5 33. big pi- 3-25 Low W 418, pi- 531 Mm di& side 1-28, pînhde 2-253-22.
RUV a r m
Craler 1-17 28,29,30 big pi- 3-17 Crater 1-1 8, 4-21, pinhole 5-32 weld
(2mmnt
Good endsearlv Crater 3.7, pinhdes 39, 31 53Q, 32 at
Top Cornier secfoir from 510 lo 16
Wty G d
Part # 1 4 1
Good l end f Pinhdea 312,23,25,28,5-18. ejeCm
..Ekri#n Pinhde 1-18, tip pinhde 1-30 Good-no(mrcherQameSerialknsb1
Datafile 06-26-16-O(M3-S1
Gbad lstart Vud 532 io 34 ends early !Good
Snial cm& 2-21, C ~ & N 3-3, 5-8, 10, Good 12, pi- end 3132 CUIWX~ and3alm~sta#mcf1sk Pinhde 3-18 Good ,.BQ u9d 1-8. craW 3-4, 4-15
1-26. a. en, 29,5-7,9, IO. 31.32 at erd, big pi- 4-10, craler
1- 18 1 Rnhde 4-29,53233 at end, craler 4-
19 Craier 3-25, pinhde 3-30.5, surface
ûood M W Pinhdes 1-20,24,2-26,27,31,3-26, 35.4-21.22.25.30.520, big pi- 1(
