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CRANFIELD UNWERSITY SCHOOL OF INDUSTRIAL AND MANUFACTURING SCIENCE PhD Thesis Academic Year 1996-97 GC CARVALHO An adaptive control system for off-line programming in robotic gas metal arc welding Supervisor: Dr. R McMaster October 1997 This thesis is submitted in fulfilment of the requirement for the degree of PhD
ABSTRACT The aim of this work was to develop an integration concept for using off-line programming in robotic gas metal arc welding of thin sheet steel. Off line -welding parameter optimization and on-line monitoring and adaptive control of process stability and torch-to-workpiece relative distance were used to ensure weld consistency. The concept developed included four main aspects: a) the use of a CAD system to design the workpiece; b) the use of a welding off-line programming system to design the welds, generate the welding parameters and to extract geometrical information from the CAD models to generate a robot program; c) the use of a graphical simulation system to simulate the robot movements; and d) the use of monitoring and adaptive control for ensuring that the required weld quality is delivered. The CAD system was chosen to be the basis for the development of the welding off-line programming system. The generation of optimized welding parameters was based on empirical welding models and the robot program generation was based on on-line programming experience. A PC based monitoring and control system was developed to provide on-line position and process control. The position control was carried out by pre-weld adjusting the initial position of the workpiece using a wire touch sensor and on-line adjusting the torch-to-workpiece distance by moving the workpiece based on the information provided by a through-the-arc sensor. The process control was carried out by automatically trimming the welding voltage such that the most stable process could be obtained. The stability of the process was estimated by using previously established monitoring indices. It was assumed that the off-line welding parameter optimization would provide the deposition rate necessary to produce the required weld quality. Successful welding control trials were performed showing the effectiveness of the adaptive control strategy. An off-line programming system has been developed and the programs generated have been tested by simulation. This showed that simulated positioning errors, produced by deliberate wrong path data, were successfully compensated for by the control system developed in this work. o DU
Page 1: CRANFIELD UNIVERSITY GC CARVALHO AN
Page 5 and 6: I dedicate this work to the memory
Page 7 and 8: FIGURES TABLES APPENDICES NOTATION
Page 9 and 10: 3.3.2.1 Welding parameters generato
Page 11 and 12: References Further reading Appendic
Page 13 and 14: Figure 3.12 Offsets for weld start
Page 15 and 16: Figure 6.31 Voltage step input test
Page 17 and 18: Figure J. 2 Plot of stand-off varia
Page 19 and 20: Table A. 1 Welding extended entity
Page 22 and 23: NOTATION A, Electrode sectional are
Page 24 and 25: Pr(ign) Possibility measure of proc
Page 26 and 27: 1. Introduction Welding is the thir
Page 28: Chapter 4 describes the on-line pos
Page 31 and 32: In pulse transfer, the welding curr
Page 33 and 34: Pd = Pv - pzh-s (2.1) where Pd is t
Page 35 and 36: the bridge. Another factor that ind
Page 37 and 38: establish the stable conditions for
Page 39 and 40: Philpott [ref. 21] developed an on-
Page 41 and 42: current peak at the moment the tip
Page 43 and 44: obot on the line must be individual
Page 45 and 46: collision detection capabilities wh
Page 47 and 48: cause dynamic variation in the seam
Page 49 and 50: Another type of robot static calibr
Page 51 and 52: 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
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0.430- - 0.380- - :r 0.330 fPR L- 0
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7.2 Tests with varying stand-off an
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Table 7.3 - Bead geometry along the
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ö a2 ., u" u,.., g .,. aucyuaac VI
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0 24-- 22-- 18 210 1 90 0 v 170 mm
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Q 34 32 3o mIm Viewing direction Fl
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ýa 34 mm JO a .. ý nn ýr "u 00 M
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22 20 18 Viewing direction Flange W
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36 33 31 o 29 t 350 310 o 290 U due
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I C, x -i 23 21 j 19 Q 240c WFS =10
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en 34 32 - vsec 30- 26- 24 + .. ++
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186
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" to design and build the hardware
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Although only linear joints were im
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for predicting possibility of bad a
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8.4 Process monitoring and control
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satisfy the required quality criter
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198
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types of joints and multi-pass weld
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[14] NEMCHINSKY, V. A. The effect o
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[38] D1LTHEY, U. , REICHELL, T. , S
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[64] KING, F-J. and HIRSCH, P. Seam
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[86] CARGNELLI, M. and ROGOWSKI, A.
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[109] KURKIN, N. S. and DRIKKER, V.
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[130] USHIO, M. , LIU, W. , MAO, W.
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[151] DAVIS, A. R. Orr-line gap det
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[177] COOK, G. E. Feedback and adap
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[201] WON, Y. J. and CHO, H. S. A f
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220
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222
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wnum: local variable which defines
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Table A. 1 - continuation List item
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Imean = a2 + b2*WFS + c2*SO + d2*SO
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TCP2 number are also defined. The o
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f ROBSET. DAT: This file is created
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Figure C. 3 - Main dialogue box of
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Choose the set of welding parameter
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Water Cooling optic fibre bundle 0
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Figure E. 1 - Main graphical screen
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242
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Table G. 2 - Interface box external
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Robot ii Controller +24W CO . +24Vd
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248
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Table 1.1 - continuation Run Vpk M
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10- y =0.001ac 0 r. dSO [mm] $0 '10
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5.00- 0.00 y=0.0019x 1000 1500 2000
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Table J. 7 - Welding data collected
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Table J. 10 - Welding data collecte
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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
Page 301 and 302:
Table K. 3 - Continuation Set-up pa
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Table K. 3 - Continuation Setup par
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