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

Global Engineering and Materials, Inc. (GEM) along with its team members, University of Illinois Urbana-Champaign (UIUC), Weber Metals, and Spirit AeroSystems, proposes to develop and demonstrate a coupled framework based on the integrated computational materials engineering (ICME) and machine learning (ML) approach to optimize the quenching process with coupled thermal, metallurgical, and mechanical analysis. The key features of our novel approach include: 1) a coupled thermodynamics and turbulent multiphase flow based FSI for quenching process simulation; 2) an immersometric FSI with adaptive mesh/quadrature refinement/coarsening for multi-part dipping of large scale structures; 3) a domain decomposition simulation module to capture the system dependent agitation mechanisms; 4) a mechanistic constitutive modeling approach with coupling of microstructure and macro-level constitutive behavior; 5) a digital property evolution module and its coupling with a zoning approach; 6) a multi-level physics-informed ML framework for quenching process modeling and uncertainty quantification; and 7) a performance informed process optimization via a surrogate modeling approach. The resulting QLAB toolkit will be firstly validated using data collected from Web Metals at coupon and component levels. The high-fidelity toolkit along with its multi-level PINNs will be demonstrated using the data collected from the V-22 and CH-53K forging parts doing and after the quenching. To further enhance the feasibility of the technology transition, we will explore the process tailoring to reduce the distortion of large scale structures.