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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Verification, Calibration, and Validation Approaches in Modeling the Mechanical Performance of Metallic Materials
Presentation Title Machine Learning Framework for Microstructure and Material State Assessment in Robotic Open-Die Forging
Author(s) Nathan Robert Bianco, Alexander Bandar, Brian Thurston, Glenn Daehn, Colton Wright, Rana Bakhtiyarzade, Tara Wagoner, Steve Niezgoda
On-Site Speaker (Planned) Nathan Robert Bianco
Abstract Scope Open-die forging is a flexible, cost-effective process for shaping large components, but it involves complex, multivariate deformation conditions that are challenging to control and optimize. The Agility Forge, developed through the NSF-HAMMER ERC, advances toward an autonomous forging platform by integrating physics-based simulation and machine learning into its control framework. Supporting various die geometries, Agility Forge also functions as an in-situ property measurement tool, acting like a Gleeble thermomechanical simulator during forging. In this study, DEFORM finite element modeling (FEM) is combined with Gaussian process regressors (GPR) to predict microstructure descriptors. Validated models are then extended to Agility Forge data to infer grain-scale features from experimental inputs. These trained models serve as a flexible, cost-effective surrogate for in-situ material property estimation and microstructure prediction during forging. This hybrid FEM–ML approach enables scalable, real-time material state awareness and represents a key step toward autonomous control of microstructure in metal forming.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation, ICME

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