Innovative Multi-Physics-based Tool to Minimize Residual Stress / Distortion in Large Aerospace Aluminum Forging Parts

Global Engineering and Materials, Inc. (GEM) and its team members, University of Illinois Urbana-Champaign (UIUC) and Professor Richard Sisson from the Center for Heat Treating Excellence at Worcester Polytechnic Institute (WPI), propose developing a coupled tool based on integrated computational materials engineering (ICME) and a machine learning (ML) approach to optimize quenching processes with coupled thermal, metallurgical, and mechanical approaches. The ML-driven ICME toolkit will utilize physics-informed neural networks (PINNs) that integrate fundamental physical principles and data from high-fidelity simulations and experiments, to generate the inter-relationship between residual stress/distortion and quenching process parameters. A high-fidelity thermal multi-phase fluid-structure interaction (FSI) model will be enhanced to simulate fluid dynamics with phase transitions and temperature fields in the quenchant tank. An immersogeometric approach will be used for efficient FSI model generation directly based on 3D workpiece CAD models with various dipping orientations. Given the temperature and phase profiles predicted from the thermal multi-phase FSI module, a residual stress and distortion prediction module will be developed by including fields mapping, temperature and phase dependent property evolution, and a user-defined material model for Abaqus. The high-fidelity IMCE and its ML-based tools will be validated using water quenching data of aluminum coupons collected from WPI.