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

Yao Lu; Yanfeng Xing; Xuexing Li; Sha Xu

doi:10.3221/IGF-ESIS.53.25

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

Authors

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

DOI:

https://doi.org/10.3221/IGF-ESIS.53.25

Keywords:

Cold metal transfer welding, Orthogonal test, Gray relational grade theory, BP neural network, Genetic algorithm

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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Published

19-07-2020

Issue

Vol. 14 No. 53 (2020): July 2020

Section

Advanced Manufacturing and Processing

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.

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How to Cite

A new approach of CMT seam welding deformation forecasting based on GA-BPNN. (2020). Frattura Ed Integrità Strutturale, 14(53), 325-336. https://doi.org/10.3221/IGF-ESIS.53.25