Interaction of Tool Wear and the Machining Affected Zone in Nickel-Based Superalloys.

该奖项支持基础研究，以了解加工镍基高温合金时刀具磨损演变和振动稳定性与切削力学之间的关系，提供规划新方法所需的知识，以延长刀具寿命并使行为更加一致。这项研究的应用将有助于降低成本，提高高价值能源和航空航天系统的质量，包括国防应用。因此，该项目促进了科学的进步，促进了国家的繁荣和福利，并有助于确保国防。此外，这项工作还包括一项计划，通过国家GEM联合会和克莱姆森妇女参与科学和工程方案的招聘工作，扩大代表性不足和少数群体参与科学研究的范围。 本研究项目的目的是通过一种新的实验方法解耦刀具磨损演变模型的可变性，并将磨损和加工参数与镍基高温合金的塑性变形层（加工影响区，MAZ）深度相关联。导出的模型将被用来模拟稳定的，鲁棒的和最低成本的路径规划。实现这一点的方法将是模拟工艺参数对磨损和切削力的影响，以及随后对塑性变形区深度的累积影响（即，MAZ）通过随机表示方法，其中模型参数表示为随机变量的信念分布。所产生的知识将被应用于推导出强大的工具路径，以提高工艺的一致性和性能。如果成功，这项工作的更广泛的影响将包括提高能源和航空航天制造商的加工经济性，并增加代表性不足的群体在科学研究中的参与。该奖项反映了NSF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。

This award supports fundamental research in understanding the relationship of tool wear evolution and vibrational stability to cutting mechanics when machining nickel-based superalloys, to provide knowledge needed to plan new approaches that can extend tool life and make behavior more consistent. Application of this research will help reduce costs and improve quality for high-value energy and aerospace systems, including defense applications. As such, this project promotes the progress of science, advances national prosperity and welfare, and helps secure national defense. Additionally, this work includes a plan for broadening participation of underrepresented and minority groups in scientific research by recruitment efforts through the National GEM consortium and Clemson's Women in Science and Engineering (WISE) programs. The objective of this research project is to decouple the variability in tool wear evolution models through a novel experimental procedure, and to relate wear and machining parameters to the plastic deformation layer (Machining Affected Zone, MAZ) depth in nickel-based superalloys. The derived models will be used to simulate stable, robust and minimum cost path plans. The approach to accomplishing this will be to model the process parameters' effect on wear and cutting forces, and the subsequent cumulative effect on the plastic deformation zone depth (i.e., MAZ) through a stochastic representation approach, whereby model parameters are represented as random-variable belief distributions. The knowledge generated will be applied to deriving robust tool paths for improving the process consistency and performance. If successful, the broader impacts of this work will include improved economy of machining for energy and aerospace manufacturers, and increased participation of underrepresented groups in scientific research.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.