CAREER: Thermomechanical Response and Fatigue Performance of Surface Layers Engineered by Finish Machining: In-situ Characterization and Digital Process Twin

点击翻译按钮获取中文摘要

Finish machining is a widely used process for precision component manufacture. Not only does it achieve required dimensional accuracy, finish machining also dictates the part surface integrity. The surface layer altered by machining subject to severe and localized thermal and mechanical loading and exhibit properties far different from the bulk material. Though shallow (order of 10 to 100 microns thick), it plays a critical role in part performance, especially, for dynamic loading. Furthermore, the surface material response in such finishing operations is complicated and challenging to study because of the small length scale and extreme deformation conditions. This Faculty Early Career Development (CAREER) award will pursue fundamental knowledge of the effects of finish machining on the behavior and performance of advanced metals such as titanium alloys, using a digital process twin (i.e., virtual representation of a physical process) approach to increase the quality and life of high-value components. The research outcomes have a potential to improve productivity and competitiveness of the US manufacturing industry, with a broad application potential in the aerospace, biomedical, and automotive sectors. The project team will collaborate closely with leading regional and national aerospace manufacturers to identify and address key technical requirements and workforce education needs. In addition, partnership with the Society of Women Engineers will be leveraged to recruit and train a more diverse workforce with full participation of female and underrepresented minority students.The core research objective of this CAREER project is the realization of model-based intelligent finish machining through the systematic study and modeling of finishing-specific material response to the thermomechanical loads in finish machining. Using a high-resolution novel process characterization testbed, the project team will perform advanced in-situ measurements of surface material response in finishing-specific conditions. Based on insights from in-situ characterizations from finish machining experiments, this study will evaluate semi-analytical digital process twin models by benchmarking their predictive performance and speed against established numerical approaches, such as finite element modeling. The project will also leverage high-resolution digital image correlation techniques to study strain localization effects during both finish machining itself, and crack initiation events during subsequent fatigue testing using micro-specimens extracted from machined surface layers. By modeling the process-induced structure response of finished surfaces in a faster and more reliable manner, the research effort will lay a groundwork for a more efficient development approach for finish machining processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.