A new approach of CMT seam welding deformation forecasting based on GA-BPNN

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Published Jul 19, 2020
DOI https://doi.org/10.3221/IGF-ESIS.53.25

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Yao Lu
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China
Yanfeng Xing
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China
Xuexing Li
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China
Sha Xu
School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China

Abstract

Welding deformation affects the quality of the welded parts. In this paper, by introducing improved back propagation neural network (BPNN), a cold metal transfer (CMT) welding deformation prediction model for aluminum-steel hybrid sheets is established. Before applying BPNN, important parameters affecting welding deformation were obtained by orthogonal test and gray relational grade theory. The accuracy of welding deformation prediction of BPNN is improved by genetic algorithm. The results show that compared with the prediction method based on traditional theory, the deformation prediction model based on GA-BPNN has higher accuracy. Predicted results were applied to the aluminum-steel CMT seam welding in the form of inverse deformation, and the deformation of the welded plate was significantly improved.

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    Issue
    Vol. 14 No. 53 (2020): July 2020
    Section
    Advanced Manufacturing and Processing

    How to Cite

    Lu, Y., Xing, Y., Li, X. and Xu, S. (2020) “A new approach of CMT seam welding deformation forecasting based on GA-BPNN”, Frattura ed Integrità Strutturale, 14(53), pp. 325–336. doi: 10.3221/IGF-ESIS.53.25.
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