Feature Engineering and Data Scaling for Spatially Resolved Post-Springback Mechanical State Predictions in Sheet Metal Forming

Abstract Data-driven sheet metal forming relies on fast, reliable predictions of quality indicators to avoid tolerance violations in downstream assembly. This study evaluates spatially resolved machine learning predictions of residual thickness, strain and stress for an S -rail forming process. An input–output dataset generated with a validated simulation model is used to compare features derived from the force–displacement curve (FDC) with material and process parameters. Results are target dependent, and baseline descriptors are sufficient for effective plastic strain, whereas adding FDC features yields modest but consistent error reductions for post-springback von Mises stress and maximum principal strain, also in critical hot spots. Learning curves assess how accuracy scales with training set size. Across targets, they show a steplike reduction in prediction error at higher training budgets, while stress state targets exhibit a smaller decrease. Overall, practical guidance is provided on feature selection and minimum sample sizes for robust, data-driven sheet metal process chains.