Data-driven Multiscale Damage and Failure Prediction

材料的损伤和失效是司空见惯的；在工程系统中预测损伤和后续失效的能力是设计的基础，当故障代价高昂甚至危及生命时，这一能力至关重要。随着制造技术变得更加先进，特别是随着添加制造的出现，几乎任何形状或形式都可以通过局部应用材料和热来制造，用于预测这些部件的机械响应的方法也必须如此。本研究中的计算建模框架将使这些先进制造技术能够通过对使用这些方法制造的零件的材料性能的严格理解而得到更广泛的应用。广泛的实验表征和验证工作将形成这个计算框架的基础。因此，这项研究将促进制造科学和对形状和形状的考虑超过生产率考虑的领域的知识，例如在生物医学和航空航天行业。这项研究带来的制造业进步将直接惠及美国经济，促进国家健康、繁荣和福利，并通过技术创新保障国防安全，例如，通过减少更轻的附加制造部件的飞机燃料消耗。这项研究所需的交叉领域，包括：制造、机械工程、材料科学和计算科学，将支持跨学科合作，从而促进现代工程教育的横向改进。作为该项目的一部分，将对高中生进行推广，以培养对工程的兴趣，将招募本科生暑期实习生进行最先进的研究，并将创建与高级建模和模拟相关的专业研究生项目。研究的预期结果是对复杂、分层材料(如金属合金)的损伤和失效的预测计算理论。这项工作建立在传统力学框架下的数据驱动、降阶和多尺度原则之上，并有可能产生一种变革性的新理论。首先，将进行基本的表征实验(包括X射线断层扫描和衍射)，以了解添加制造的金属中材料微结构和机械性能之间的关系。这些信息将用于校准微机械模型，模拟将用于填充合成微结构及其机械响应的数据库。在此基础上，发展了一种新的基于降阶方法的并行多尺度理论，能够同时捕捉几何和材料响应的非线性。该方法将查询第一阶段建立的数据库中的机械信息，并使用这些数据来预测损坏和故障，特别是使用添加剂制造的金属部件。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。

Damage and failure of materials is commonplace; the ability to predict damage and subsequent failure in engineered systems is foundational to design, and critically important when failures are expensive and even life-threatening. As manufacturing technologies become more advanced, particularly with the advent of additive manufacturing where nearly any shape or form can be made by local application of material and heat, so too must the methods used to predict the mechanical response of these components. The computational modeling framework in this research will enable a wider application of these advanced manufacturing technologies thorough a rigorous understanding of the material performance of parts made with these methods. An extensive experimental characterization and validation effort will form the basis of this computational framework. As such, this research will promote manufacturing sciences and knowledge for the fields where shape and form considerations outweigh production rate concerns, e.g., in biomedical and aerospace industries. The manufacturing advances enabled by this research will directly benefit the U.S. economy, advance national health, prosperity, and welfare, and secure national defense through technological innovations, e.g., through reduced aircraft fuel consumption from lighter additively manufactured parts. The intersection of domains required for this research, including: manufacturing, mechanical engineering, materials science, and computational sciences, will support interdisciplinary collaboration that can lead to crosscutting improvements in engineering education for the modern age. As part of this project, outreach to high school students will be performed to foster interest in engineering, undergraduate summer interns will be recruited to conduct state-of-the-art research, and specialized graduate student projects will be created related to advanced modeling and simulation.The anticipated outcome of the research is a predictive computational theory for damage and failure of complex, hierarchical materials such as metal alloys. The effort builds on data-driven, reduced order, and multiscale principles under the traditional framework of mechanics with the potential for a transformative new theory. Initially, fundamental characterization experiments (including x-ray tomography and diffraction) will be conducted to understand the relationship between material microstructures and mechanical properties in additively manufactured metals. This information will be used to calibrate micromechanical models, and simulations will be used to populate a database of synthetic microstructures and their mechanical response. From this, a new concurrent multiscale theory based on reduced-order methods will be developed, capable of capturing nonlinearity both in geometric and material response. This method will query the database constructed in the first phase for mechanical information and use that data to predict damage and failure, particularly for metals parts made with additive manufacturing.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.