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

精加工是一种广泛应用于精密零件制造的工艺。它不仅达到要求的尺寸精度，精加工也决定了零件表面的完整性。加工后的表层受到严重的局部热载荷和机械载荷的影响，表现出与大块材料截然不同的性能。虽然很浅（10到100微米厚），但它在零件性能中起着关键作用，特别是在动态加载时。此外，由于长度尺度小，变形条件极端，这种精加工过程中的表面材料响应复杂且具有挑战性。该学院早期职业发展（Career）奖将追求精加工对钛合金等先进金属的行为和性能影响的基础知识，使用数字过程孪生（即物理过程的虚拟表示）方法来提高高价值部件的质量和寿命。研究成果对提高美国制造业的生产率和竞争力具有潜力，在航空航天、生物医药、汽车等领域具有广泛的应用潜力。项目团队将与领先的地区和国家航空航天制造商密切合作，以确定和解决关键技术需求和劳动力教育需求。此外，将利用与女工程师协会的伙伴关系，在女性和代表性不足的少数族裔学生的充分参与下，招聘和培训更多样化的劳动力。本CAREER项目的核心研究目标是通过对精加工过程中特定精加工材料对热力载荷响应的系统研究和建模，实现基于模型的智能精加工。利用高分辨率的新型工艺表征测试平台，项目团队将对表面材料在特定加工条件下的响应进行先进的原位测量。基于精加工实验现场表征的见解，本研究将通过将其预测性能和速度与已建立的数值方法（如有限元建模）进行基准测试来评估半分析式数字过程孪生模型。该项目还将利用高分辨率数字图像相关技术，在精加工过程中研究应变局部化效应，以及在随后的疲劳测试中使用从加工表面层中提取的微试样研究裂纹起裂事件。通过更快、更可靠地建模加工表面的过程诱导结构响应，将为更有效的精加工工艺开发方法奠定基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.