Machine Learning–Enhanced Multiscale Modeling of Spatially Tailored Materials

空间定制材料是由两种或更多种不同材料组成的金属陶瓷复合材料。每种材料的体积分数在空间中不断变化。与传统复合材料相比，这些复合材料具有传统复合材料的优势，因为它们为设计人员提供了更多的机会来设计其性能以适应操作条件和环境。由于这些复合材料具有跨越多个长度尺度的特征，因此通常有必要开发能够以不同分辨率水平描述物理现象的预测模型，例如，在纳米尺度和微米尺度上。该奖项支持开发一种创新的多尺度方法，通过机器学习技术增强并得到实验数据的支持，以研究机械和热载荷下空间定制材料的力学。所提出的方法将加速用于汽车，航空航天和生物医学行业的下一代金属陶瓷复合材料的设计。此外，该奖项将利用大学计划来支持：（1）数据科学与工程的本科教学;（2）招募女性，代表性不足的少数民族和LGBTQ学生;以及3）扩展到K-12学生。 目前，空间剪裁材料的数值建模的最新实践使用微观力学原理来桥接从所考虑的较低尺度到宏观尺度的差距。然而，大多数方法不能考虑分子界面相互作用和微观结构的不确定性，这两者都必须被考虑到准确地预测材料响应在更高的长度尺度。即使这些考虑得到了解决，也会导致难以明确地推导出有效的材料特性和本构关系或失效关系。该项目利用数据科学技术和机器学习来克服空间定制的钛合金-二硼化钛金属-陶瓷材料系统的多尺度材料建模中的这些挑战。该项目将导致以下一般成果：（1）在分层多尺度建模中开发数据启用方法，以链接和接口信息（2）开发一种有效的方法来产生考虑微观结构不确定性的异质复合材料的均匀化微观尺度模型;（3）开发一个自适应机器学习框架，随着数据积累而更新;以及（4）该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识产权评估的支持。优点和更广泛的影响审查标准。

Spatially tailored materials are metal-ceramic composites consisting of two or more different materials. The volume fraction of each material continuously changes in space. Compared to traditional composites, these composites offer advantages over traditional ones for they provide more opportunities to designers to fashion their performance for the operating conditions and environment. Since these composites have features spanning multiple length scales, it is usually necessary to develop predictive models capable of describing physical phenomena at different levels of resolution, e.g., at the nanoscale as well as the microscale. This award supports the development of an innovative multiscale method, enhanced by machine learning techniques and supported by experimental data, to investigate the mechanics of spatially tailored materials under mechanical and thermal loading. The proposed method will accelerate the design of the next generation of metal-ceramic composites for use in the automotive, aerospace, and biomedical industries. In addition, the award will leverage university programs to support: (1) undergraduate teaching and learning in data science and engineering; (2) recruitment of female, underrepresented minority, and LGBTQ students; and 3) outreach to K-12 students. Current state-of-the-art practices for numerical modeling of spatially tailored materials use the principles of micromechanics to bridge the gap from the lower scales under consideration to the macroscale. However, most methods cannot account for the molecular interfacial interactions and the microstructure uncertainties, both of which must be considered to accurately predict material responses at the higher length scales. And even when these considerations are addressed, it results in the difficulty of explicitly deriving effective material properties and constitutive or failure relationships. This project utilizes data science techniques and machine learning to overcome these challenges in the multiscale material modeling of a spatially tailored titanium alloy-titanium diboride metal-ceramic material system. The project will lead to the following general outcomes: (1) development of a data-enabled approach in hierarchical multiscale modeling to link and interface information (including uncertainties) across multiple length and time scales; (2) development of an efficient way to generate homogenized microscale models of heterogeneous composites that account for microstructure uncertainties; (3) development of an adaptive machine learning framework that updates with data accumulation; and (4) design of multiscale experiments under a variety of thermomechanical loading conditions.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.