QPOT: A Multiphysics Analysis based Quenching Process Optimization Tool for Large Forging Parts

Naval Aviation aircraft procurement faces cost and schedule challenges where one of the major contributors is the high scrap rate of large airframe aluminum forging parts. The parts were rejected for geometrical non-conformance, due to distortion induced during quenching stage. To address the critical challenge, Intelligent Automation Inc. (IAI) and collaborators propose to develop an innovative multi-physics analysis and Machine Learning (ML) based optimization tool that covers three disciplines (thermal, mechanical, and metallurgical) to reduce residual stresses and distortion within large aluminum forging parts introduced during the quenching process. In Phase I, the research team will work together to develop the foundation modules of the simulation tool, including fluid flow and heat transfer analysis, precipitation modeling and residual stress and distortion simulation. Moreover, the team will formulate the ML framework that can combine experimental data to improve simulation accuracy and enhance computational efficiency. Moreover, a detailed test plan will be developed, and calibration and validation tests will be conducted based on representative part with simple geometries.