DMREF/GOALI/Collaborative Research: Physics-Informed Artificial Intelligence for Parallel Design of Metal Matrix Composites and their Additive Manufacturing

这项设计材料以革命和工程我们的未来（DMREF）研究使物理信息的人工智能（AI）设计与陶瓷颗粒增强的金属材料（金属基复合材料）及其增材制造（3D打印）。相对于没有陶瓷增强物的相同金属材料，这种材料在更高温度下可以表现出上级机械性能。增材制造为由金属基复合材料制成的高性能、轻质结构部件提供了前所未有的制造能力。然而，金属基复合材料的设计及其增材制造在很大程度上是通过昂贵、耗时的试错方法来进行的;这些部件的质量保证也受到类似的挑战。人工智能引导的材料及其制造的设计和认证可以显着降低这些技术的时间和成本障碍。该计划中进行的基础研究将填补关键空白，使人工智能能够发现和优化这些材料及其制造，从而将部署时间和成本减少一半，以满足材料基因组计划的愿景。外展计划和多样性，公平和包容性计划包括人工智能制造课程课程，涵盖幼儿园-研究生，其中包括从该计划开发的示例问题和工具。亚特兰大和盐湖湖城高中教师和来自代表性不足的少数民族的学生将在这些课程中获得实践经验和指导。该研究保持并扩展了支持合金，陶瓷及其复合材料基础研究的强大计划;支持大学之间自由流动的互动模式（格鲁吉亚理工学院和犹他州），初创企业（GOALI合作伙伴Elementum 3D）和国家实验室（空军研究实验室）;扩大对自动化材料制造研究的投资，以确保美国到2030年成为该领域的领导者;在适当的情况下，所有这些都使用计算方法，数据分析，机器学习和自主实验3D表征。该研究计划使物理信息人工智能（AI）驱动的金属基复合材料及其增材制造的并行设计成为可能。发现和优化新材料及其并行增材制造（AM）的AI概念有望进一步彻底改变AM，但尚未实现。基础研究旨在实现人工智能自主发现和优化材料及其制造，将部署时间和成本减少一半，以满足材料基因组计划的愿景。将填补五个关键的数据驱动算法空白：1）数据分析-解释-策展算法，以实现从必要的过程-结构-属性数据源自动化，血统数据策展。2)自动化数据清理和数据库连接的算法，以便AI可以在新的数据源或数据特征被纳入研究问题时修改和附加数据空间。3)跨多个长度和时间尺度自动化数据特征映射以完成过程-结构-属性数据本体的算法。4)提高AI性能的数据特征工程算法。5)流程-结构-属性机器学习模型，学习多个嵌套子模型之间的全局关系。将推进基于物理的模型和实验，以预测和验证它们在发现和优化金属基复合材料及其在多个长度和时间尺度上的增材制造方面的效用。高通量一维、二维和三维表征数据分析将自动化。GOALI合作伙伴Elementum 3D将提供一项技术经济基线研究，将一种用于增材制造的新型金属基复合材料商业化，作为该计划进步的总体评估指标。新的AM测试工件的开发将使地球仪的研究人员受益。为自动化数据工作流程而开发的协议和标准可以通过增加对高通量和高保真数据源的访问，使世界各地的材料科学和工程研究人员受益，包括机器学习模型和人工智能知识系统，用于各种材料和制造工艺。该项目由土木工程部，工程局（ENG）的机械和制造创新（CMMI），计算机和信息科学与工程局（CISE）的信息和智能系统部（IIS），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 Designing Materials to Revolutionize and Engineer our Future (DMREF) research enables physics-informed artificial intelligence (AI) design of metal materials reinforced with ceramic particles (metal matrix composites) and their additive manufacturing (3D printing). Such materials can exhibit superior mechanical performances at higher temperatures relative to the same metal material without ceramic reinforcements. Additive manufacturing provides unprecedented fabrication capability for high performance, lightweight structural components made from metal matrix composite materials. However, the design of metal matrix composites and their additive manufacturing is largely performed with expensive, time consuming trial and error methodologies; quality assurance of such parts is similarly challenged. AI-guided design and qualification of materials and their manufacturing can significantly lower the time and cost barriers to such technologies. The basic research performed in this program will fill critical gaps to enable AI discovery and optimization of these materials and their manufacturing toward reducing deployment times and costs by half, to meet the Materials Genome Initiative vision. The outreach programs and diversity, equity, and inclusion plans include AI manufacturing course curricula spanning kindergarten - graduate which include example problems and tools developed from this program. Atlanta and Salt Lake City high school teachers and students from underrepresented minority populations will receive hands-on experience and instruction in these curricula. The research maintains and expands robust programs supporting fundamental research in alloys, ceramics, and their composites; support modalities for free-flowing interactions among universities (Georgia Tech and Utah), start-up ventures (GOALI partner Elementum 3D), and national laboratories (Air Force Research Laboratory); expand investments in automated materials manufacturing research to ensure the U.S. is the leader in the field by 2030; all using, when appropriate, computational methods, data analytics, machine learning, and autonomous experimental 3D characterization.This research program enables physics-informed artificial intelligence (AI) - driven parallel design of metal matrix composites and their additive manufacturing. The concept of AI that discovers and optimizes new materials and their Additive Manufacturing (AM) in parallel promises to further revolutionize AM but is yet to be realized. Basic research is to enable autonomous AI discovery and optimization of materials and their manufacturing toward reducing deployment times and costs by half, to meet the Materials Genome Initiative vision. Five critical data-driven algorithmic gaps will be filled: 1) data analysis-interpretation-curation algorithms to enable automatic, pedigreed data curation from requisite process-structure-property data sources. 2) Algorithms that automate data cleaning and concatenation of databases so that AI can modify and append the data spaces when new data sources or data features are incorporated into a research problem. 3) Algorithms that automate data feature mapping across multiple length and time scales to complete process-structure-property data ontologies. 4) Data feature engineering algorithms that improve the AI performance. 5) Process-structure-property machine learning models that learn global relationships across multiple nested submodels. Physics-based models and experiments will be advanced to predict and verify their utility in discovering and optimizing metal matrix composites and their additive manufacturing at multiple length and time scales. High throughput one-dimensional, two-dimensional, and three-dimensional characterization data analyses will be automated. GOALI partner Elementum 3D will provide a techno-economic baseline study of commercializing a new metal matrix composite for additive manufacturing to be used as an overall assessment metric for the advancements made in this program. The development of a new AM test artifact will benefit researchers around the globe. The protocols and standards developed for automating data workflows can benefit materials science and engineering researchers around the world by increasing access to high-throughput and high-fidelity data sources, including machine learning models and AI knowledge systems, for all kinds of materials and manufacturing processes.This project is jointly funded by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Directorate for Engineering (ENG), the Division of Information and Intelligent Systems (IIS) in the Directorate for Computer and Information Science and Engineering (CISE), and the Division of Materials Research (DMR) in the Directorate for Mathematical and Physical Sciences (MPS).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.