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

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Published July 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

A new approach of CMT seam welding deformation forecasting based on GA-BPNN. (2020). Fracture and Structural Integrity, 14(53), 325-336. https://doi.org/10.3221/IGF-ESIS.53.25
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How to Cite

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