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Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network

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Abstract

Extrusion connection is a new method of forming and manufacturing heterogeneous welded sheets. The factors that affected the bonding quality are the forming temperature, the extrusion ratio, and the guiding angle of the die, which has brought trouble to the evaluation of bonding strength and quality. A method to establish a predicted model for the bonding strength of welded sheets by integrating finite element simulations, process experiments, and artificial neural networks was developed. Finite element simulations were used to verify the process experiments and provided training data sets for the artificial neural networks. The BP neural network was used to predict the bonding strength. Due to the randomness of the weight and threshold of the BP neural network, its predicted accuracy needs to be improved, in which genetic algorithms were used to optimize consequently. The results showed that the genetic algorithm neural network model had higher reliability, and the predicted accuracy was 99.5%. Compared with the traditional BP neural network, the predicted accuracy was improved by 5.78%, and the error was reduced to 0.5%. It has good generalization ability and provides a new way for intelligent reliability evaluation of high performance heterogeneous welded sheets via extrusion.

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Data availability

The data obtained in the framework of this study are available to the journal upon request.

Abbreviations

n :

The number of test set samples

o i :

Predicted output value of the ith test set sample

y i :

Expected output value of the ith test set sample

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Funding

This paper was supported by the Key Laboratory of Micro-systems and Micro-structures Manufacturing, Ministry of Education, Harbin Institute of Technology (2020KM005), and the Fundamental Research Foundation for Universities of Heilongjiang Province (LGYC2018JQ011).

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Authors and Affiliations

  1. School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, 150040, China

    Lei Gao, Feng Li, Peng Da Huo & Chao Li

  2. Key Laboratory of Micro-systems and Micro-structures Manufacturing of Ministry of Education, Harbin Institute of Technology, Harbin, 150001, China

    Jie Xu

Authors
  1. Lei Gao
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  2. Feng Li
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  3. Peng Da Huo
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  4. Chao Li
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Contributions

Lei Gao: conceptualization, methodology, writing-original draft preparation, and experimental scheme design. Li Feng: writing, reviewing, and editing. Peng Da Huo, Chao Li, and Jie Xu: algorithm help.

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Correspondence to Feng Li or Jie Xu.

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Gao, L., Li, F., Da Huo, P. et al. Accurate prediction of the extrusion forming bonding reliability for heterogeneous welded sheets based on GA-BP neural network. Int J Adv Manuf Technol 117, 765–774 (2021). https://doi.org/10.1007/s00170-021-07797-7

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  • DOI: https://doi.org/10.1007/s00170-021-07797-7

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