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[164] RICHARDS, R. J. An introduction to dynamics and control. Cambridge: Longman Scientific & Technical, 1979. [165] CORNISH, H. L. et al. Computerised process control: A management decision. New York : Hobbs, Dorman, 1968. [166] KASATKIN, O. G. Experimental design system for solving the problems of welding operational parameters optimization. IN: "Weld quality The role of computers", Proceedings of the 1IW International Conference on Improved Control with Special Reference to Computer Technology, 4-5 July 1988, Vienna, Austria, 169-173. [167] MONTGOMERY, D. C. Design and analysis of experiments. 2°d Edition. New York: John Wiley & Sons, 1983. [168] HUNTER, J. J. , BRYCE, G. W. and DOHERTY, J. On line control of the arc welding process. IN: TWI International Conference on Developments in Mechanised Automated and Robotic Welding, 18-20 November 1980, London, paper 37. [169] ALFARO, S. C. A. Mathematical modelling of narrow gap submerged arc welding. <strong>Cranfield</strong> Institute of Technology, PhD Thesis, 1989. [170] HARRIS, P. and SMITH, B. L. Factorial techniques for weld quality prediction. Metal Construction, 15(11), 1983,661-666. [171] LAMBERT, J. and GILSTON, P. Hot-wire GTAW for nuclear repairs. Welding Journal, 69(9), 1990,45-52. [172] JOHNSON, D. E. Harnessing the power of multiple regression. Chemical Engineering, 96(11), November 1989,176-188. [173] WALPOLE, R. E. and MYERS, RH. Probability and statistics for engineers, 4th Edition. New York: Mcmillan, 1989. [174] ASTROM, K. J. , WITTENMARK, B. Adaptive control. Reading: Addison- Wesley, 1989. [175] COOK, G. E., ANDERSEN, K., BARRET, R. J. Keynote address - feedback and adaptive control in welding. IN: "Recent Trends in Welding Science and Technology" Proceedings of 2°d international Conference on Trends in WeldingResearch. 14-18 May 1989, Gatlinburg, Tennessee, USA, 891- 903. [176] DOi1MANIDIS, C. C. Multiplexed and distributed control of automated welding. IEEE Control Systems, 14(4), 1994,13-24. 215
[177] COOK, G. E. Feedback and adaptive control of process variables in arc welding. IN: TWI International Conference on Developments in Mechanised Automated and Robotic Welding, 18-20 November 1980, London, Paper 32. [178] HUISSON, J. P. et al. Design of an integrated robotic welding system. IN: "International Trends in Welding Science and Technology", Proceedings of the 3rd International Conference on Trends in Welding Research. 1-5 June 1992, Gatlinburg, Tennessee, USA, 915-919. [179] HUISSON, J. P. et al. Multi-variable control of robotic gas metal arc welding. Journal of Materials Processing Technology, 43(1), 1994,1-12. [180] AGAPAKIS, J. E. et al. Joint tracking and adaptive robotic welding using vision sensing of the weld joint geometry. Welding Journal, 65(11), 1986, 33-41. [181] NISHAR, D. V. et al. Adaptive control of temperature in arc welding. IEEE Control Systems, 14(4), 1994,4-12. [182] SONG, J. -B., HARDT, D. E. Dynamic modelling and adaptive control of the gas metal arc welding process. Transactions of the ASME - Journal of Dynamic Systems, Measurement, and Control, 116(3), September 1994, 405-413. [183] SUGITANI, Y., MAO, W., USHIO, M. Adaptive control of weld bead shape utilizing arc sensor in one side GMA W process with backing plate. International Institute of Welding Document, IIW Doc. XII-1360-94,1994. [184] OHSHIMA, K. et al. Knowledge based information processing, for the penetration control in the robotic welding by fuzzy inference. IIW Document MI- 13 75-94,1994. [185] USHIO, M. (ed. ) The state-of-the-art and the subjects of arc welding automation in various industries in Japan. Technical Commission on Welding Processes, JWS, IIW Doc. XII-1359-94,1994. [186] USHIO, M. (ed. ) Automation technology of arc welding. Technical Commission on Welding Processes, JWS, I1W Doc. MI- 1471-96,1996. [187] NAUCK, D., KLAWONN, F., KRUSE, R. Fuzzy sets, fuzzy controllers, and neural networks. Scientific Journal of the Humboldt-<strong>University</strong> of Berlin, Series Medicine 41(4), 1992,99-120. [188] SHAW, H. F. , LUCAS, W. The potencial of neural networks in welding. IN: TWI Conference on Computer Technology in Welding, 15-16 June 1994, Paris, France, Paper 52. 216
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CRANFIELD UNIVERSITY GC CARVALHO AN
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ABSTRACT The aim of this work was t
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I dedicate this work to the memory
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FIGURES TABLES APPENDICES NOTATION
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3.3.2.1 Welding parameters generato
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References Further reading Appendic
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Figure 3.12 Offsets for weld start
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Figure 6.31 Voltage step input test
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Figure J. 2 Plot of stand-off varia
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Table A. 1 Welding extended entity
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NOTATION A, Electrode sectional are
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Pr(ign) Possibility measure of proc
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1. Introduction Welding is the thir
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Chapter 4 describes the on-line pos
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In pulse transfer, the welding curr
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Pd = Pv - pzh-s (2.1) where Pd is t
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the bridge. Another factor that ind
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establish the stable conditions for
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Philpott [ref. 21] developed an on-
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current peak at the moment the tip
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obot on the line must be individual
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collision detection capabilities wh
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cause dynamic variation in the seam
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Another type of robot static calibr
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spatter the gas nozzle is particula
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Table 2.1: Joint positioning tolera
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In order to minimise the undesirabl
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c) Ultrasonic sensors; d) Through-t
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where K,, K2, K3 and K4 are constan
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Philpott [refs. 21,131], based on t
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centre. This is attributed to the m
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technique must be employed to preve
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" digital hardware, which is basica
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" Maximum value, W.: Wmý = max(W )
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process studied over a small range.
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In-process welding control is a muc
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volume caused by the presence of a
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SHIELDING GAS IN CONSUMABLE ELECTRO
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Arc voltage Anode Arc length I Anod
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computed ideal procedure optimisnti
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CONSTANT CURRENT POWER SOURCE `j' O
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Original Surface New Surface Electr
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otating welding wire direction weld
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3.1.1.1 Robot errors A robot arm is
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3.2.2 Programming error correction
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3.3.2 Off-line programming module I
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Pen is the weld penetration (side p
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a. 2) Geometrical constraints from
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Step 11 Step 12 Step 13 Step 14 Ste
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Step 21 Step 22 If the wire feed sp
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the Y'-axis results in a positive "
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_ p1e0a - pORoba m31 Ilp! - poll (3
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of a robot in its zero position12 w
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frame points to the front of the ro
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CAD Off-line (AutoCAD) programming
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Z Surface 4 A Y 7 M2 Open Edge Join
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WCF World Co-ordinates Frame 2a = J
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x Torch Co-ordinates Frame o Indica
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vo Ö 't7 Y7 't O m O 'S O O S O S
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ýý aý 0 oA aW ö A O Zt A OO 't
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Ti 9ý-! i4 *Tx t ap31 jx ý- ymin
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errors', (i. e. joint positioning,
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was considered to be outside the sc
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Table 4.3 - Linguistic representati
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The introduction of such a reduced
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Table 4.8 - Final welding process c
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the collection of welding data corr
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Step 4- Calculate the confidence of
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Z Table Co-ordinates Frame Y X Robo
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5.3 Monitoring system The monitorin
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the torch end. Only one degree of f
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Current: 500 Position: 234.5 5.7.3
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I" va JI ºr ö C 3 r" r" rl f_ £-
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IgurC a. H -I UI qur vCiSUJ JpCCU C
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126
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Table 6.1 - Welding trials carried
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Table 6.3 - Coefficients of Ogunbiy
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Note that the calibration model sho
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Table 6.6 - Welding parameters used
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constant set-up welding parameters
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stickout lengths and the temperatur
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of the dip mode of metal transfer.
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which would become active by settin
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In section 4.2.5 it has been mentio
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300 280 260 240 rd 35- 3D 25 20 15
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Figure 6.5 - Measured "versus" Pred
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i S 0 -0.1 -0.2 -0.3 -0.7- 68 10 12
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32.0 E 31.5 y 31.0 30.5 30.0 j 29.5
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35 - 30 p 15 10 0.00 2.60 5.42 8.23
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40 35 30 25 20 15 10 A+iIII+F0.17 2
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30 28 26 24 22 SO_act [mm] DipR [mo
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22 20 18 SO_act [mm] DipR [ohm/100]
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!: 30 25 20t 10 5-- 0 Dip Transfer
Page 189 and 190: 0.430- - 0.380- - :r 0.330 fPR L- 0
Page 191 and 192: 7.2 Tests with varying stand-off an
Page 193 and 194: Table 7.3 - Bead geometry along the
Page 195 and 196: ö a2 ., u" u,.., g .,. aucyuaac VI
Page 197 and 198: 0 24-- 22-- 18 210 1 90 0 v 170 mm
Page 199 and 200: Q 34 32 3o mIm Viewing direction Fl
Page 201 and 202: ýa 34 mm JO a .. ý nn ýr "u 00 M
Page 203 and 204: 22 20 18 Viewing direction Flange W
Page 205 and 206: 36 33 31 o 29 t 350 310 o 290 U due
Page 207 and 208: I C, x -i 23 21 j 19 Q 240c WFS =10
Page 209 and 210: en 34 32 - vsec 30- 26- 24 + .. ++
Page 211 and 212: 186
Page 213 and 214: " to design and build the hardware
Page 215 and 216: Although only linear joints were im
Page 217 and 218: for predicting possibility of bad a
Page 219 and 220: 8.4 Process monitoring and control
Page 221 and 222: satisfy the required quality criter
Page 223 and 224: 198
Page 225 and 226: types of joints and multi-pass weld
Page 227 and 228: [14] NEMCHINSKY, V. A. The effect o
Page 229 and 230: [38] D1LTHEY, U. , REICHELL, T. , S
Page 231 and 232: [64] KING, F-J. and HIRSCH, P. Seam
Page 233 and 234: [86] CARGNELLI, M. and ROGOWSKI, A.
Page 235 and 236: [109] KURKIN, N. S. and DRIKKER, V.
Page 237 and 238: [130] USHIO, M. , LIU, W. , MAO, W.
Page 239: [151] DAVIS, A. R. Orr-line gap det
Page 243 and 244: [201] WON, Y. J. and CHO, H. S. A f
Page 245 and 246: 220
Page 247 and 248: 222
Page 249 and 250: wnum: local variable which defines
Page 251 and 252: Table A. 1 - continuation List item
Page 253 and 254: Imean = a2 + b2*WFS + c2*SO + d2*SO
Page 255 and 256: TCP2 number are also defined. The o
Page 257 and 258: f ROBSET. DAT: This file is created
Page 259 and 260: Figure C. 3 - Main dialogue box of
Page 261 and 262: Choose the set of welding parameter
Page 263 and 264: Water Cooling optic fibre bundle 0
Page 265 and 266: Figure E. 1 - Main graphical screen
Page 267 and 268: 242
Page 269 and 270: Table G. 2 - Interface box external
Page 271 and 272: Robot ii Controller +24W CO . +24Vd
Page 273 and 274: 248
Page 275 and 276: Table 1.1 - continuation Run Vpk M
Page 277 and 278: 10- y =0.001ac 0 r. dSO [mm] $0 '10
Page 279 and 280: 5.00- 0.00 y=0.0019x 1000 1500 2000
Page 281 and 282: Table J. 7 - Welding data collected
Page 283 and 284: Table J. 10 - Welding data collecte
Page 287 and 288: 20.00- 15. M y=0.0047x - 1.7922 10.
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266
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Table K. 1- continuation Set-up par
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Table K. 1 - continuation Set-up pa
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Table K. 2 - continuation Set up pa
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Table K. 2 - Continuation Set-up pa
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Table K. 3 - Continuation Set-up pa
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Table K. 3 - Continuation Setup par
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