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

陶瓷材料具有优异的机械、电学、光学、化学、热学和生物相容性，在恶劣条件下得到了广泛的应用。然而，由于陶瓷在撞击时会破碎而不是变形，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，它的生产率并不令人满意，对材料在切割下的行为，特别是在原子尺度上的理解仍然是难以捉摸的。该奖项是为了在对材料失效的更好理解的基础上找到陶瓷材料的最佳加工条件。这一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策略获得的。这种方法方便了先进陶瓷的加工，而不需要额外的后处理，这是昂贵和耗时的，因此达到了行业要求的生产率。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，如发动机机体、相机镜头、高能激光和生物医学植入物成为可能，这对美国的工业和经济有利。这项研究吸引了来自历史上代表性不足的群体的学生参与研究经验，利用研究生工程研究学者和科学与工程女性等项目。这项合作研究结合了实验和原子模拟，通过考虑三种典型的陶瓷材料：两种硬质陶瓷，蓝宝石和氧化锆，以及一种软质陶瓷，磷酸二氢钾，来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。陶瓷的超精密加工依赖于其晶体结构的各向异性及其对发生韧性到脆性转变的临界切割深度的影响。切削实验旨在量化不同切削条件下残余应力和亚表面损伤的变化，而原子模拟则提供了在原子尺度上对陶瓷在加工过程中的延性和脆性行为的详细了解。采用分子动力学方法进行原子模拟。特别是，基于原子-连续介质耦合的多尺度方法能够在更真实和接近实验的条件下进行模拟。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，该算法确定了将残余应力和亚表面损伤和开裂降至最低所需的最佳切割条件。机器学习的预测又通过加工实验和模拟得到了验证。有了这些知识，在控制残余应力和亚表面损伤的同时，采用积极的粗切削来满足可伸缩材料的去除率，然后采用精加工延性模式切割来消除裂纹并使表面光滑。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.