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Residual Learning-Based Synthetic Data for Hybrid Metamodeling in the Stamping of an Automotive Door Panel
Abstract:
Hybrid twins and residual-learning strategies are increasingly used to reconcile the broad coverage of physics-based simulations with the fidelity of production measurements. In industrial stamping, however, truly matched simulation-experiment cases are scarce, while large-scale monitoring data are clustered around a narrow operating window. This work proposes an industrially practical hybrid metamodel in which the discrepancy (ignorance) model is trained exclusively from approximate residuals computed at in-domain production inputs, considering a surrogate of a thermal enabled AutoForm Sigma Design of Experiments (DoE). To prevent uncontrolled extrapolation, learning and evaluation are restricted to an explicitly validated domain defined through multivariate kNN support in standardized space derived from the DoE cloud. The residual model is selected through five-fold cross-validation on 10,446 in-domain production samples and then retrained on the full approximate-residual set. A small set of five matched cases is kept as an external check based on true residuals. The resulting hybrid predictor enables the generation of synthetic, experiment-informed corrected data while retaining the DoE coverage required for downstream modelling tasks.
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103-111
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April 2026
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