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A comparative study of surrogate models for predicting process failures during the sheet metal forming process of advanced high-strength steel

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Abstract

Considering the importance of failure prediction in the sheet metal forming design process, the ability to predict these failures by the four most common surrogate techniques, namely response surface methodology (RSM), radial basis function (RBF), kriging, and artificial neural network (ANN), was investigated. Firstly, a finite element model (FEM), which can substitute for the physical deep drawing and precisely predict thinning and rupture, has been developed. To ensure the accuracy of the FE model, a comparison between simulation results and experimental results is performed. In this study, the construction of training and test data is carried out by the Latin hypercube design (LHD) method via numerical simulation. Secondly, the four surrogate models are developed to predict thinning and fracture as a function of the six most critical parameters namely, blank holder force, punch section radius, die section radius, die fillet radius, blank thickness, and die blank friction coefficient. Finally, the performance and accuracy of these models are demonstrated by a goodness-of-fit test.

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Authors and Affiliations

  1. Mechanical Engineering Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Iliass El Mrabti, Abdelhadi El Hakimi & Abdelhamid Touache

  2. Industrial Techniques Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Abderrahim Chamat

Authors
  1. Iliass El Mrabti
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  2. Abdelhadi El Hakimi
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  3. Abdelhamid Touache
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  4. Abderrahim Chamat
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Correspondence to Iliass El Mrabti.

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Appendix

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Table 14 Training data set for the both outputs
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El Mrabti, I., El Hakimi, A., Touache, A. et al. A comparative study of surrogate models for predicting process failures during the sheet metal forming process of advanced high-strength steel. Int J Adv Manuf Technol 121, 199–214 (2022). https://doi.org/10.1007/s00170-022-09319-5

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