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Intelligent Weld Manufacturing: Role of Integrated Computational Welding Engineering Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Improving Stability of Welding Model with ME-ELM

Authors : Jianxiong Ye, Han Ye, Zhigang Li, Xingling Peng, Jinlan Zhou, Bo Guo

Published in: Transactions on Intelligent Welding Manufacturing

Publisher: Springer Singapore

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Abstract

Welding shape is important in evaluating welding quality, but accurate predictive model is hard to achieve, because welding is a complex nonlinear process, and the sampled data are inevitably contaminated. Extreme learning machine (ELM) is used to construct a single-hidden layer feedforward network (SLFN) in our study, for improving stability of welding model, M-estimation is combined with ELM and a new algorithm named ME-ELM is developed; researches indicate that it works more effective than BP and other variants of ELM in reducing influence, furthermore, it can improve the model’s anti-disturbance and robustness performance even if the data are seriously contaminated. Real TIG welding models are constructed with ME-ELM, by comparing with BP, multiple nonlinear regression (MNR), and linear regression (LR), conclusions can be gotten that ME-ELM can resist the interference effectively and has the highest accuracy in predicting the welding shape.

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Metadata
Title
Improving Stability of Welding Model with ME-ELM
Authors
Jianxiong Ye
Han Ye
Zhigang Li
Xingling Peng
Jinlan Zhou
Bo Guo
Copyright Year
2018
Publisher
Springer Singapore
Book
Transactions on Intelligent Welding Manufacturing

Print ISBN: 978-981-10-7042-6

Electronic ISBN: 978-981-10-7043-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7043-3_4