NDLI: Use of ANN to simulate the effects of welding process parameters in curved steel plates: residual stresses, strains and distortions

Content Provider	IEEE Xplore Digital Library
Author	Klein, R.L. Klinkhachorn, P. Mucino, V.H. Awang, M.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., West Virginia Univ., Morgantown, WV, USA (Klein, R.L.; Klinkhachorn, P.)
Abstract	In large steel fabrication industries such as shipbuilding, and high-speed train guideways, the problem of residual stresses and overall distortion has been and continues to be a major issue. In the last few decades, various research efforts have been directed at the control of the welding process parameters aiming to reduce distortions and residual stresses. Yet, in actual practice, large amounts of resources are still required to rework welds. These costs increase production costs and delay work completion. In the work reported here the Finite Element Method (FEM) is used to simulate the welding process in two-steps; first a non-linear heat transfer step that yields the dynamic temperature distribution throughout the weld seam and the plates, and second, the elasto-plastic analysis, which yields the residual stresses, strains, and the displacements. The responses focused upon were those along the longitudinal cross sections after the welded piece had cooled down to room temperature. An artificial neural network is trained using FEM simulation data for a wide variety of geometric and process parameter combinations. Then, the resulting neural networks is shown to be capable of predicting the welding response without having to carry out a computationally complex, time consuming full finite element analysis. This concept is shown to be a highly effective and efficient way to predict welding responses for welding process design purposes.
Sponsorship	Lane Dept. Comput. Sci. & Electr. Eng. Electr. & Comput. Eng., WVU Inst. Technol. Adv. Power Eng. Res. Center IEEE Control Syst. Soc. IEEE West Virginia Sect
Starting Page	29
Ending Page	33
File Size	288634
Page Count	5
File Format	PDF
ISBN	0780376978
ISSN	00942898
DOI	10.1109/SSST.2003.1194524
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Welding Steel Residual stresses Capacitive sensors Artificial neural networks Nonlinear distortion Costs Finite element methods Fabrication Industrial training
Content Type	Text
Resource Type	Article

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