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Optimization of Stamping Process Parameters Based on Improved GA-BP Neural Network Model

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International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Reasonable process parameters are the key measures to ensure the quality of stamping products. In order to reduce the risk of cracking and wrinkling of stamping products, an improved genetic algorithm is proposed and used to optimize the weights and thresholds of the BP neural network(BPNN). A surrogate model combining an improved genetic algorithm and BPNN(IGA-BPNN)is developed. Taking double C as the research object, the training samples and test samples are extracted through Latin hypercube. The training output of IGA-BPNN model is obtained by AutoForm simulation, and the mapping relationship between process parameters and forming quality is established. Then the mapping relationship is optimized by IGA to obtain the optimal process parameters. The results show that this method reduces the wrinkling of the flange edge of double C and obviously improves the forming quality.

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Code Availability

The sample data in this article is generated by AutoForm software, and the original program code is written by the author himself. The improvement and operation of the program are realized in MATLAB. The full datasets, as well as the source codes, can be available from the corresponding author with reasonable request.

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Acknowledgements

This work is supported by the Key Laboratory of Mechanical Structure Optimization & Material Application Technology of Luzhou in China (Optimization design of blank holders in in nonisothermal stamping based on CAE, SCHYZSA-2022-04).

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Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, No. 111, North Section 1, Second Ring Road, Jinniu District, Chengdu, 610031, Sichuan, People’s Republic of China

    Yanmin Xie, Wei Li, Cheng Liu, Meiyu Du & Kai Feng

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  1. Yanmin Xie
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  2. Wei Li
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  3. Cheng Liu
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  5. Kai Feng
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Correspondence to Yanmin Xie.

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Xie, Y., Li, W., Liu, C. et al. Optimization of Stamping Process Parameters Based on Improved GA-BP Neural Network Model. Int. J. Precis. Eng. Manuf. 24, 1129–1145 (2023). https://doi.org/10.1007/s12541-023-00811-w

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