Abstract
Stamping is the main manufacturing process for sheet metal parts. However, during the stamping process, based on excessive blank holder force, unreasonable mold design, and other factors, it is easy to generate defects such as cracks in the drawing area and flange wrinkles. In this paper, a novel hybrid model based on a restricted Boltzmann machine and back-propagation neural network is proposed and its validity is verified through different testing functions. Additionally, an improved multi-objective particle swarm optimization (MOPSO) method based on a crowding operator is proposed and compared to several powerful existing algorithms. The proposed method was applied to the process optimization of a double-C part. The sensitivity of the forming quality to different process parameters was analyzed and a novel index was used to describe quality changes. A mapping relationship between the process parameters and forming quality was established based on the proposed hybrid model. Furthermore, optimal process parameters were obtained using MOPSO. The results demonstrated that the proposed method significantly reduces flange wrinkles without excessive thinning and improves the uniformity of formed parts.
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This work was supported by the Sichuan Science and Technology Program (2020YFH0078; 2019YFG0313).
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The sample data used in this study were generated by the AutoForm software and the original program code was taken from https://emoo.cs.cinvestav.mx/software.php. The program was optimized and executed in MATLAB. The full datasets, as well as the source code, can be obtained from the corresponding author upon reasonable request.
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Xie, Y., Du, L., Zhao, J. et al. Multi-objective optimization of process parameters in stamping based on an improved RBM–BPNN network and MOPSO algorithm. Struct Multidisc Optim 64, 4209–4235 (2021). https://doi.org/10.1007/s00158-021-03056-1
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DOI: https://doi.org/10.1007/s00158-021-03056-1