Optimization of stamping process parameters based on an improved particle swarm optimization–genetic algorithm and sparse auto-encoder–back-propagation neural network model

Abstract

Stamping is a very important manufacturing process. To optimize the process parameters, a hybrid surrogate model based on the back-propagation neural network and sparse auto-encoder is proposed and compared with classical surrogate models to verify its reliability. Furthermore, the hybrid improved particle swarm optimization–genetic algorithm, based on chaos theory, is proposed and compared with other algorithms. A double-C part is used as an engineering example to verify the proposed method. The Latin hypercube sampling method is used for sampling and the response value is obtained by AutoForm simulation software. On this basis, the hybrid surrogate model is used to establish the mapping relationship between the forming quality of the double-C part and the stamping process parameters. The optimal stamping process parameters are obtained through the improved hybrid algorithm. The results demonstrate that the wrinkling of the optimized double-C part is significantly reduced and the forming quality is improved.

KEYWORDS:

• Stamping
• sparse auto-encoder
• neural network
• hybrid algorithm
• forming quality

Acknowledgements

The authors are grateful for support from the Sichuan Science and Technology Program (2020YFH0078) and from the Key Laboratory of Mechanical Structure Optimization & Material Application Technology of Luzhou (Optimization design of blank holders in nonisothermal stamping based on CAE, SCHYZSA-2022-04).

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The stamping simulation result data in this article is obtained by AutoForm, and the proposed optimization algorithm code is realized by MATLAB. Readers interested in more details are encouraged to contact the corresponding author via email. The data or implementation code is available upon request.

Additional information

Funding

This work was 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).