Fracture and Structural Integrity e-ISSN 1971-8993 Open access
Advanced Manufacturing and Processing Vol. 14, No. 53 (2020)

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

Author and affiliation details
  • Yao Lu School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China
  • Yanfeng Xing Corresponding author School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, China smsmsues@163.com
  • 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
10.3221/IGF-ESIS.53.25
Pages
325-336
Published
Licence
CC BY 4.0
Published inVol. 14, No. 53 (2020)Go to the issue

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.

Keywords

Article history

  1. ReceivedUntil the next stage: 19 days
  2. Revision requestedUntil the next stage: 85 days
  3. RevisedUntil the next stage: 30 days
  4. AcceptedUntil the next stage: 56 days
  5. Published

How to cite

APA

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

IEEE

Y. Lu, Y. Xing, X. Li, and S. Xu, “A new approach of CMT seam welding deformation forecasting based on GA-BPNN,” Fracture and Structural Integrity, vol. 14, no. 53, pp. 325–336, 2020, doi: 10.3221/IGF-ESIS.53.25.

MLA

Lu, Yao, et al. “A new approach of CMT seam welding deformation forecasting based on GA-BPNN.” Fracture and Structural Integrity, vol. 14, no. 53, 2020, pp. 325–336. https://doi.org/10.3221/IGF-ESIS.53.25.

Chicago

Lu, Yao, Yanfeng Xing, Xuexing Li, and Sha Xu. 2020. “A new approach of CMT seam welding deformation forecasting based on GA-BPNN.” Fracture and Structural Integrity 14 (53): 325–336. https://doi.org/10.3221/IGF-ESIS.53.25.

Harvard

Lu, Y., Xing, Y., Li, X. and Xu, S. (2020) ‘A new approach of CMT seam welding deformation forecasting based on GA-BPNN’, Fracture and Structural Integrity, 14(53), pp. 325–336. doi: 10.3221/IGF-ESIS.53.25.

Vancouver

Lu Y, Xing Y, Li X, Xu S. A new approach of CMT seam welding deformation forecasting based on GA-BPNN. Fracture and Structural Integrity. 2020;14(53):325-336. doi:10.3221/IGF-ESIS.53.25

BibTeX

@article{lu2020, author = {Lu, Yao and Xing, Yanfeng and Li, Xuexing and Xu, Sha}, title = {A new approach of CMT seam welding deformation forecasting based on GA-BPNN}, journal = {Fracture and Structural Integrity}, year = {2020}, volume = {14}, number = {53}, pages = {325--336}, doi = {10.3221/IGF-ESIS.53.25}, url = {https://doi.org/10.3221/IGF-ESIS.53.25}, issn = {1971-8993} }

RIS

TY - JOUR AU - Lu, Yao AU - Xing, Yanfeng AU - Li, Xuexing AU - Xu, Sha TI - A new approach of CMT seam welding deformation forecasting based on GA-BPNN JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2020 DA - 2020/07/19 VL - 14 IS - 53 SP - 325 EP - 336 DO - 10.3221/IGF-ESIS.53.25 UR - https://doi.org/10.3221/IGF-ESIS.53.25 SN - 1971-8993 ER -

Licence

Creative Commons Attribution 4.0 International

May be shared and adapted, provided appropriate credit is given. Copyright © 2020 Yao Lu, Yanfeng Xing, Xuexing Li, Sha Xu.

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

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