Research on stamping forming prediction of aluminum alloy sheet based on RBF neural network

In order to accurately predict and reduce the possible defects in the stamping process of an aluminum alloy sheet, the simulation data of the sheet thickness for the 6016 aluminum alloy in the stamping process were obtained by the Hill’48 yield criterion based on finite element ABAQUS/Explicit solve...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-12, Vol.2396 (1), p.12038
Main Authors: Zhao, Pengjing, Yang, Yo-Lun, Gao, Peng, Jiao, Jingpin
Format: Article
Language:English
Subjects:
Aluminum alloys
Aluminum base alloys
Blankholders
Coefficient of friction
Computer simulation
Finite element method
Mathematical models
Metal sheets
Neural networks
Optimization
Physics
Process parameters
Radial basis function
Stamping
Thinning
Yield criteria
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Summary:In order to accurately predict and reduce the possible defects in the stamping process of an aluminum alloy sheet, the simulation data of the sheet thickness for the 6016 aluminum alloy in the stamping process were obtained by the Hill’48 yield criterion based on finite element ABAQUS/Explicit solver. Taking blank holder force, friction coefficient, stamping speed, and die clearance as input parameters, the radial basis function (RBF) network model for predicting the maximum thinning rate of the stamping aluminum alloy sheet was established. The results show that the RBF network model constructed in this paper has high precision and can reflect the complex relationship between the stamping process parameters and the maximum thinning rate well by comparing the finite element simulation and neural network prediction results. It is of great significance to improve the optimization efficiency of the stamping process of the aluminum alloy sheet and reduce the actual experimental cost.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2396/1/012038