Neural networks for parameters prediction of an electromagnetic forming process of FeP04 steel sheets

Abstract

An electromagnetic forming process of FeP04 steel sheets is studied over a wide range of variation of technological parameters aiming at finding optimal values providing maximum deformation of the part. The process parameters considered in this paper are deformed part diameter (d), thickness of specimen (t), distance coil–specimen (D), number of coil turns (N), capacitance of capacitor bank (C), and the charging voltage (V). Each specimen was freely deformed on a die of ring type through a unique combination of input parameters included in the study, and the maximum depth of the deformed parts center (h) was considered output parameter. Radial basis function neural networks are used for modeling the functional dependence between technological parameters and the resulting deformation of the material. Modeling results indicate an average mean square error around 4 % and a correlation between experimental data and neural model output exceeding 0.99. Sensitivity analysis reveals significant input parameters that decisively influence the output of the model. Simulation results are in good agreement with the experimental data, suggesting that neural networks can be considered a viable modeling alternative to existing approaches.