Novel multi-physics-based modeling of a quenching process with thermal-metallurgical-mechanical interactions in aluminum components

Large aluminum forging parts are increasingly used in aerospace structures to enable structural unitization. In the fabrication of heat treatable aluminum parts for the aerospace industry, quenching is a crucial step to suppress the precipitation to retain the supersaturation of the solid solution, control the distortion, and minimize the residual stress in aluminum alloys. Because of the complex interaction between temperature, phase-transformation, and stress/strain relation that depends on the temperature distribution and the microstructure of the workpiece, there is no performance informed quenching process that can be applied reliably to reduce the high scrap rate of airframe aluminum forging parts with a significant amount of residual stress and distortion. The development of a quicker and more reliable qualification and certification procedure is so important given the stringent constraints on cost and schedule. The primary goal of this study is to develop a multi-physics tool to perform simulations with optimized quenching parameters to achieve minimum distortion. A high-fidelity thermal multi-phase fluid-structure interaction (FSI) model is applied to simulate fluid dynamics and temperature fields in the quenchant tank. The developed immersogeometric modeling approach is used next for an efficient model generation of a 3D workpiece with various dipping orientations. Given the temperature and pressure profiles predicted from the FSI based heat transfer module, residual stress and distortion prediction modules are developed by including temperature and pressure fields mapping and temperature and strain rate dependent property evolution via Abaqus' user-defined subroutines. Verification and demonstration studies are performed using aluminum coupons dipped into a quenching tank of two different orientations with and without agitation. Time histories of the temperature and residual stress fields were predicted to explore the relationship between the process and performance.