Collaborative Research: Understanding Subsurface Damage and Residual Stress during Ultra-Precision Machining of Ceramics

合作研究：了解陶瓷超精密加工过程中的次表面损伤和残余应力

• 批准号: 2008563
• 负责人: Sangkee Min
• 金额: $ 33.23万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008563&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Subsurface; Damage

Ceramic materials have found various applications, especially under harsh conditions, thanks to their superior mechanical, electrical, optical, chemical, thermal, and biocompatible properties. However, since ceramics shatter upon impact rather than deform, manufacturing ceramic components with complex structures of high-quality surfaces is a challenge. Ultra-precision machining of ceramics has found a way to overcome this challenge by cutting or removing very tiny amounts of material. However, its productivity is not satisfactory and an understanding of the material behavior under cutting, especially at atomic scale, remains elusive. This award is to find optimized machining conditions for ceramic materials based on an improved understanding of material failure. This understanding is obtained by a combined strategy of state-of-the-art experiment and atomistic simulation approaches coupled with machine learning algorithms. This approach facilitates the machining of advanced ceramics without the need for extra post-processing, which is expensive and time consuming and, thus, achieves industry-required productivity. Moreover, by improving the fabrication process and damage control of ceramic materials, high quality ceramic components such as engine blocks, camera lenses, high energy lasers, and biomedical implants are possible, which benefits U.S. industry and economy. This research engages students from historically underrepresented groups in research experiences, leveraging programs such as Graduate Engineering Research Scholar and Women in Science and Engineering.This collaborative research combines experiment and atomistic simulations to understand how residual stress and subsurface damage form during ultra-precision machining of ceramics by considering three representative ceramic materials; two hard ceramics, sapphire and zirconia, and one soft ceramic, potassium dihydrogen phosphate. Ultra-precision machining of ceramics depends on the anisotropy in their crystal structure and its influence on the critical depth-of-cut where the ductile-to-brittle transition occurs. The cutting experiments are designed to quantify changes in residual stress and subsurface damage under various cutting conditions while the atomistic simulations provide a detailed understanding of the ductile and brittle behaviors of ceramics at the atomic scale during machining. Molecular dynamics methodology is employed for atomistic simulations. In particular, the multiscale approach, based on the atomistic-continuum coupling, enables performing simulations in more realistic and near-experimental conditions. Moreover, experiments and simulations provide sampling conditions for the machine learning algorithm based on K-nearest neighbor calculations, which determine the optimal cutting conditions necessary to minimize residual stress and subsurface damage and cracking. The machine learning predictions are, in turn, verified by machining experiments and simulations. With this knowledge, aggressive rough cutting is applied to meet scalable material removal rate while controlling residual stress and subsurface damage, followed by finish ductile-mode cutting to remove cracks and smooth out the surface.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.

陶瓷材料由于其上级的机械、电学、光学、化学、热学和生物相容性，已经发现了各种应用，特别是在恶劣条件下。然而，由于陶瓷在冲击时破碎而不是变形，因此制造具有高质量表面的复杂结构的陶瓷部件是一个挑战。陶瓷的超精密加工已经找到了一种通过切割或去除非常少量的材料来克服这一挑战的方法。然而，它的生产率是不令人满意的，在切削下的材料行为的理解，特别是在原子尺度上，仍然是难以捉摸的。该奖项旨在基于对材料失效的更好理解，为陶瓷材料寻找最佳加工条件。这种理解是通过最先进的实验和原子模拟方法与机器学习算法相结合的组合策略获得的。这种方法有助于加工先进的陶瓷，而不需要额外的后处理，这是昂贵和耗时的，因此，实现了行业所需的生产率。此外，通过改进陶瓷材料的制造工艺和损伤控制，高质量的陶瓷部件，如发动机缸体，相机镜头，高能激光器和生物医学植入物都是可能的，这有利于美国的工业和经济。本研究利用研究生工程研究学者和科学与工程女性等项目，让历史上代表性不足的群体的学生参与研究经验。本合作研究结合实验和原子模拟，通过考虑三种代表性陶瓷材料，了解陶瓷超精密加工过程中残余应力和亚表面损伤的形成;两种硬陶瓷，蓝宝石和氧化锆，和一种软陶瓷，磷酸二氢钾。陶瓷的超精密加工取决于其晶体结构的各向异性及其对发生韧脆转变的临界切削深度的影响。切削实验旨在量化各种切削条件下的残余应力和亚表面损伤的变化，而原子模拟提供了一个详细的了解在原子尺度上的陶瓷加工过程中的韧性和脆性行为。分子动力学方法用于原子模拟。特别是，多尺度的方法，基于原子连续耦合，使模拟更现实和接近实验条件。此外，实验和模拟为基于K-最近邻计算的机器学习算法提供了采样条件，该算法确定了最小化残余应力和表面下损伤和开裂所需的最佳切削条件。机器学习的预测，反过来，通过加工实验和模拟验证。在此基础上，采用了激进的粗切削，以满足可扩展的材料去除率，同时控制残余应力和表面下的损伤，然后进行最终的延性模式切削，以消除裂纹并使表面平滑。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

Studying Crack Generation Mechanism in Single-Crystal Sapphire During Ultra-precision Machining by MD Simulation-Based Slip/Fracture Activation Model

• DOI: 10.1007/s12541-023-00776-w
• 发表时间: 2023-03
• 期刊: International Journal of Precision Engineering and Manufacturing
• 影响因子: 1.9
• 作者: S. Kwon;A. Nagaraj;Dalei Xi;Yiyang Du;Dae Nyoung Kim;Woo Kyun Kim;S. Min

Effect of crystallography on residual stresses during ultra-precision machining of sapphire

• DOI: 10.1016/j.cirp.2022.04.004
• 发表时间: 2022-04
• 期刊: CIRP Annals
• 作者: A. Nagaraj;Sangkee Min

Understanding of Residual Stress and Subsurface Damage by 2- Step Machining of Single Crystal Sapphire

通过单晶蓝宝石两步加工了解残余应力和亚表面损伤

• 发表时间: 2022
• 期刊: 19th International Conference on Precision Engineering
• 作者: Nagaraj, Aditya;Min, Sangkee
• 通讯作者: Min, Sangkee

Studying crack generation mechanism of single-crystal sapphire during ultra-precision machining by MD simulation-based slip/fracture activation model

基于MD模拟的滑移/断裂激活模型研究单晶蓝宝石超精密加工过程中裂纹产生机制

• 发表时间: 2022
• 期刊: International Conference on Precision Engineering and Sustainable Manufacturing (PRESM2022
• 作者: Kwon, Suk Bum;Nagaraj, Aditya;Xi, Dalei;Du, Yiyang;Kim, Dae Nyoung;Kim, Woo Kyun;Min, Sangkee
• 通讯作者: Min, Sangkee