Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals

Journal of Materials Processing Technology

Volumn 202, Issue 1-3, 2008, Pages 464-474

  Pal, Sukhomay ^a   Pal, Surjya K ^a   Samantaray, Arun K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial neural network; Multiple regression analysis; PMIGW; Response surface methodology; Weld strength monitoring

Indexed keywords

ELECTRIC CURRENTS; INERT GAS WELDING; NEURAL NETWORKS; REGRESSION ANALYSIS; STRENGTH OF MATERIALS; TENSILE STRESS;

ARC SIGNALS; RESPONSE SURFACE METHODOLOGY; ROOT MEAN SQUARE (RMS); WELD STRENGTH MONITORING;

WELDS;

EID: 42949096408     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2007.09.039     Document Type: Article

References (43)

1. Adolfsson S., Bahrami A., Bolmsjo G., and Claesson I. On-line quality monitoring in short-circuit gas metal arc welding. Weld. J. 78 (1999) 59-73
2. Andersen K., Cook G.E., Karsai G., and Ramaswamy K. Artificial neural networks applied to arc welding process modeling and control. IEEE Trans. Ind. Appl. 26 (1990) 824-830
3. Arata Y., Inoue K., Futamata M., and Toh T. Investigation on welding arc sound: vibration analysis of base metal during welding. Trans. JWRI 10 (1981) 39-45
4. Bao M., and Ume I.C. Three-dimensional ray tracing of laser ultrasound for weld penetration sensing. J. Acoust. Soc. Am. 115 (2004) 1565-1571
5. Barborak D., Conrardy C., Madigan B., and Paskell T. Through-arc process monitoring techniques for control of automated gas metal arc welding. Proceedings of the International Conference on Robotic and Automation, vol. 4, IEEE. Detroit, MI (1999) 3053-3058
6. Carlson N.M., and Johnson J.A. Ultrasonic sensing of weld pool penetration. Weld. J. 67 (1988) 239-246
7. Chen W.H., and Chin B.A. Monitoring weld penetration using infrared sensing techniques. Weld. J. 69 (1990) 181-185
8. Chi S.C., and Hsu L.C. A fuzzy radial basis function neural network for predicting multiple quality characteristics of plasma arc welding. IFSA World Congress & 20th NAFIPS International Conference, vol. 5. Vancouver, Canada (2001) 2807-2812
9. Chin B.A., Madsen N.H., and Goodling J.S. Infrared thermography for sensing the arc welding process. Weld. J. 62 (1983) 227-234
10. Chu Y.X., Hu S.J., Hou W.K., Wang P.C., and Marin S.P. Signature analysis for quality monitoring in short circuit GMAW. Weld. J. 83 (2004) 336-343
11. Cook G.E. Feedback and adaptive control in automated arc welding systems. Met. Construction 13 (1981) 551-556
12. Cook G.E. Through the arc sensing of arc welding. Proceedings Conference Production Research Technology (1983), SAE Publication 141-151
13. Cook G.E., Andersen K., Fernandez K.R., Shepard M.E., and Wells A.M. Electric arc sensing for robot positioning control. In: Lane J.D. (Ed). Robotic Welding (1987), IFS Publications Ltd., London 182-216
14. Cook G.E., Barnett R.J., Andersen K., and Strauss A.M. Weld modeling and control using artificial neural networks. IEEE Trans. Ind. Appl. 31 (1995) 1484-1491
15. Datta, S., Bandyopadhyay, A., Pal, P.K., in press. Modeling and optimization of features of bead geometry including percentage dilution in submerged arc welding using mixture of flux and fused slag, J. Adv. Manuf. Technol., doi:10.1007/s00170-006-0894-7.
16. Di L., Srikanthan T., Chandel R.S., and Katsunori I. Neural-network based self-organized fuzzy logic control for arc welding. Eng. Appl. Artif. Intell. 14 (2001) 115-124
17. Goldberg F. Inductive seam-tracking improves mechanized and robotic welding. Proceedings of International Conference on Automation and Robotisation in Welding and Allied Processes (1985), International Institute of Welding, France 393-400
18. Grad L., Grum J., Polajnar I., and Slabe J.M. Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding. Int. J. Mach. Tools Manuf. 44 (2004) 555-561
19. Gunaraj V., and Murugan N. Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. J. Mater. Process. Technol. 88 (1999) 266-275
20. Guu A., and Rokhlin S. Arc weld process control using radiographic sensing. Mater. Eval. 50 (1992) 1344-1348
21. Hughes R.V., and Walduck R.P. Electromagnetic arc path control I plasma welding. In: Lane J.D. (Ed). Robotic Welding (1987), IFS Publications Ltd., London 2244-2263
22. Johnson J.A., Carlson N.M., Smartt H.B., and Clark D.E. Process control of GMAW: sensing of metal transfer mode. Weld. J. 70 (1991) 91-99
23. Kang S.I., Kim G.H., and Lee S.B. A study on the horizontal fillet welding using neural networks. Proceedings of the third International Conference on Knowledge-based Intelligent Information Engineering Systems, IEEE. Australia (1999) 217-221
24. Kim I.S., Son J.S., Lee S.H., and Yarlagadda P.K.D.V. Optimal design of neural networks for control in robotics arc welding. Rob. Comput.-Integr. Manuf. 20 (2004) 57-63
25. Kim I.S., Jeong Y.J., Lee C.W., and Yarlagadde P.K.D.V. Prediction of welding parameters for pipeline welding using an intelligent system. J. Adv. Manuf. Technol. 22 (2006) 713-719
26. Lee J., and Um K. A comparison in a back-bead predication of gas metal arc welding using multiple regression analysis and artificial neural networks. Opt. Laser Eng. 34 (2000) 149-158
27. Li D., Yonglun S., and Feng Y. On line monitoring of weld defects for short-circuit gas metal arc welding based on the self-organize feature map neural networks. Proc. Int. Joint Conf. Neural Netw. 5 (2000) 239-244
28. Lightfoot M.P., Bruce G.J., McPherson N.A., and Woods K. The application of artificial neural networks to weld-induced deformation in ship plate. Weld. J. 84 (2005) 23-30
29. Minitab Inc., 2000. User Manual of MINITAB™ Statistical Software, Release 13.31. Minitab Inc., State College, PA, USA, 2000.
30. Munezane Y., Watanabe T., Iochi A., and Sekino T. A sensing system for arc welding. In: Lane J.D. (Ed). Robotic Welding (1987), IFS Publications Ltd., London 265-274
31. Nagarajan S., Chen W.H., and Chin B.A. Infrared sensing for adaptive arc welding. Weld. J. 68 (1989) 462-466
32. Nagesh D.S., and Datta G.L. Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J. Mater. Process. Technol. 123 (2002) 303-312
33. Ohshima K., Yabe M., Akita K., Kugai K., Kubota T., and Yamane S. Sensor fusion using neural network in robotic welding. Ind. Appl. Conf. IEEE 2 (1995) 1764-1769
34. Quero J.M., Millan R.L., and Franquelo L.G. Neural network approach to weld quality monitoring. Proceedings of 20th International Conference on Industrial Electronics, Control and Instrumentation, IEEE, vol. 2 (1994) 1287-1291
35. Quinn T.P., Smith C., Mccowan C.N., Blachowiak E., and Madigan R.B. Arc sensing for defects in constant voltage gas metal arc welding. Weld. J. 79 (1999) 322-328
36. Rajasekaran S., Kulkarni S.D., Mallya U.D., and Chaturvedi R.C. Droplet detachment and plate fusion characteristics in pulsed current gas metal arc welding. Weld. J. 78 (1998) 254-269
37. Rokhlin S. In-process radiographic evaluation of arc welding. Mater. Eval. 47 (1989) 219-224
38. Saini D., and Floyd S. An investigation of gas metal arc welding sound signature for on-line quality control. Weld. J. 77 (1998) 172-179
39. Sforza P., and Blasiis D.D. On-line optical monitoring system for arc welding. NDT&E Int. 35 (2002) 37-43
40. Siewert T., Samardzic I., and Klaric S. Application of an on-line weld monitoring system. Proceedings of the first International Conference on Advanced Technologies for Developing Countries. Slavonski Brod, Croatia (2002)
41. Sweet L.M. Sensor-based control systems for arc welding robots. Rob. Comp.-Integr. Manuf. 2 (1985) 125-133
42. Takeshita K. Model equation for predicting the tensile strength of resistance-brazed joints. Weld. J. 79 (2000) 261-265
43. Wang J., Kusumoto K., and Nezu K. Microweld penetration monitoring techniques by arc sensing. Proceedings of International Conference on Advanced Intelligent Mechatronics IEEE/ASME, vol. 2 (2003) 1027-1030