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

职业：精加工表面层的热机械响应和疲劳性能：原位表征和数字工艺孪生

• 批准号: 2143806
• 负责人: Julius Schoop
• 金额: $ 50.73万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143806&HistoricalAwards=false
• 关键词: CAREER; Thermomechanical; Response; Fatigue; Performance

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.

精加工是精密零件制造中广泛使用的工艺。精加工不仅能达到所需的尺寸精度，还决定了零件表面的完整性。表面层因机械加工而发生变化，受到严重的局部热负荷和机械负荷的影响，并且表现出与块状材料截然不同的性能。虽然很浅（厚度为 10 至 100 微米），但它对零件性能起着至关重要的作用，特别是对于动态载荷。此外，由于长度尺度小和极端变形条件，此类精加工操作中的表面材料响应非常复杂且难以研究。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，使用数字工艺孪生（即物理工艺的虚拟表示）方法来提高高价值部件的质量和寿命。研究成果有望提高美国制造业的生产力和竞争力，在航空航天、生物医学和汽车领域具有广泛的应用潜力。该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，以确定并解决关键技术要求和劳动力教育需求。此外，将利用与女工程师协会的合作伙伴关系，招募和培训更加多元化的劳动力，并让女性和代表性不足的少数族裔学生充分参与。该职业项目的核心研究目标是通过对精加工特定材料对精加工中热机械载荷的响应进行系统研究和建模，实现基于模型的智能精加工。项目团队将使用高分辨率的新颖工艺表征测试台，对特定精加工条件下的表面材料响应进行高级现场测量。基于精加工实验的现场表征的见解，本研究将通过针对有限元建模等现有数值方法对半解析数字过程孪生模型的预测性能和速度进行基准测试来评估半解析数字过程孪生模型。该项目还将利用高分辨率数字图像相关技术来研究精加工过程中的应变局部化效应，以及使用从加工表面层提取的微型样本进行后续疲劳测试过程中的裂纹萌生事件。通过以更快、更可靠的方式对成品表面的工艺引起的结构响应进行建模，这项研究工作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 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Novel Approach for In-Situ Characterization and Probabilistic Prediction of Cutting Tool Fatigue in Machining of Ti-6Al-4V

• DOI: 10.1016/j.mfglet.2023.10.002
• 发表时间: 2023-10
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Avery Hartley;Jenna Money;J. Schoop

Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

机械加工残余应力建模中基于物理的不确定性量化

• DOI: 10.1016/j.procir.2023.03.025
• 发表时间: 2023
• 期刊: Procedia CIRP
• 作者: Hasan, Md Mehedi;Schoop, Julius
• 通讯作者: Schoop, Julius

A Review of Constitutive Models and Thermal Properties for Nickel-based Superalloys Across Machining-Specific Regimes

特定加工状态下镍基高温合金本构模型和热性能的综述

• DOI: 10.1115/1.4056749
• 发表时间: 2023
• 期刊: Journal of Manufacturing Science and Engineering
• 作者: Thornton, E-Lexus;Zannoun, Hamzah;Vomero, Connor;Caudill, Daniel;Schoop, Julius
• 通讯作者: Schoop, Julius

Analysis of burr formation in finish machining of nickel-based superalloy with worn tools using micro-scale in-situ techniques

• DOI: 10.1016/j.ijmachtools.2023.104030
• 发表时间: 2023-05
• 期刊: International Journal of Machine Tools and Manufacture
• 影响因子: 14
• 作者: H. Zannoun;J. Schoop