Metals 2026, 16(8), 880; https://doi.org/10.3390/met16080880 - 7 Aug 2026
Abstract
The main objective of this paper is to present a comparative analysis between the finite element method (FEM) and artificial neural networks (ANNs) for predicting sheet metal springback, while addressing the “black-box” nature of machine learning through explainable AI (XAI). To achieve this
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The main objective of this paper is to present a comparative analysis between the finite element method (FEM) and artificial neural networks (ANNs) for predicting sheet metal springback, while addressing the “black-box” nature of machine learning through explainable AI (XAI). To achieve this novelty, a multi-layer perceptron (MLP) architecture was implemented and evaluated against numerical simulations during the bending of a hat-shaped profile. The experimental framework utilized dual-phase HCT600X steel (0.8 mm thickness), supplemented by deep-drawing DC06 and high-strength RAK40/70 steels to ensure dataset diversity and robust generalization capability. A key contribution of this work is the integration of local SHAP (Shapley additive explanations) analysis to interpret the ANN outputs, allowing for a precise quantification and rank ordering of how individual material, design, and process parameters govern the resulting springback angle. The developed MLP model (comprising two hidden layers with five neurons each) achieved high predictive fidelity, yielding an overall correlation coefficient R = 0.99074 alongside robust error metrics (RMSE and MAE).
Full article
(This article belongs to the Special Issue Characterization and Modeling of Microstructure Evolution During Metallic Material Processing)
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