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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Acknowledgements
The work is partially founded by the Open Project Program of Jiangxi Province Key Laboratory of Precision Drive & Control (PLPDC-KFKT-201625), the National Natural Science Foundation of China (51665016), the JiangXi Province Science Foundation (20151BAB207047).
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Ye, J., Ye, H., Li, Z., Peng, X., Zhou, J., Guo, B. (2018). Improving Stability of Welding Model with ME-ELM. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-10-7043-3_4
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DOI: https://doi.org/10.1007/978-981-10-7043-3_4
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