Robust surrogate modeling for glass forming process

A critical challenge in manufacturing process optimization is that simulation-based surrogate predictive models often fail when confronted with real-world measurement uncertainties. In this study, we present a robust surrogate modeling approach applicable to simulation-based manufacturing process optimization, while accounting for real-world measurement uncertainties. Unlike previous predictive methods that focus solely on tuning prediction accuracy or incorporate robustness through model-specific techniques, our methodology simultaneously optimizes for both accuracy and robustness, requiring only simulation data for training. Using glass forming as a case study, we quantitatively evaluate six machine learning algorithms under temperature measurement uncertainties of \((\pm)\)3°C. In our experiments, Multi-layer perceptrons achieve the best overall performance with mean squared error of nodal deviation \((<)\) 0.2 while maintaining high robustness (0.6). Our approach generates a diverse set of Pareto-optimal solutions that allows post-training-and-optimization selection of the ideal model based on specific manufacturing requirements, eliminating the need to predefine the exact balance between accuracy and robustness before model development. This work represents a significant advancement in bridging the gap between idealized simulations and practical industrial applications by systematically accounting for measurement uncertainties in a model-agnostic manner.