Towards hybrid and explainable data-driven models for forming processes: addressing the gap between simulation, process data, and model validation

Finite element method simulations are foundational to forming process design, but their utility for machine learning is often limited by idealizations that create a gap with stochastic, real-world manufacturing conditions. This paper investigates the systematic integration of simulation-based knowledge into machine learning pipelines to bridge this gap. A structured review of 56 publications reveals systemic limitations in current research, which we classify as the reality, validation, and trust gaps. In response, this work formulates four research questions to address these gaps, examining them through use cases in forging, blanking, and deep drawing. The analysis demonstrates that the impact of simulation simplifications is highly task-dependent, requiring a tailored approach to model selection and data integration. This paper contributes to the methodological discourse by proposing a framework for simulation-informed machine learning. It argues for a shift in focus from maximizing physical realism to a “fitfor-purpose” approach, where simulation complexity is strategically aligned with the requirements of the downstream machine learning task. The paper outlines a path toward more robust, interpretable, and industrially transferable modeling strategies in forming technology.