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

DMREF/GOALI/协作研究：基于物理的人工智能用于金属基复合材料及其增材制造的并行设计

基本信息

批准号：
2119640
负责人：
Aaron Stebner
金额：
$ 117.76万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119640&HistoricalAwards=false
关键词：
DMREF GOALI Collaborative Research Physics

项目摘要

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.

这种设计材料彻底改变并设计了我们的未来（DMREF）研究，可实现具有陶瓷颗粒（金属基质复合材料）及其添加剂制造（3D打印）的金属材料的物理知识人工智能（AI）设计。相对于相同的金属材料，这种材料可以在较高的温度下表现出卓越的机械性能，而无需陶瓷钢筋。添加剂制造提供了前所未有的制造能力，可用于高性能，轻巧的结构组件，由金属基质复合材料制成。但是，金属基质复合材料及其添加剂制造的设计在很大程度上以昂贵的，耗时的试验和错误方法进行。此类部分的质量保证也同样受到挑战。 AI引导的材料设计和资格及其制造可以大大降低此类技术的时间和成本障碍。该计划中执行的基础研究将填补关键空白，以使AI发现和优化这些材料及其制造能够减少部署时间和成本，从而满足材料基因组倡议的愿景。外展计划以及多样性，公平和包容计划包括跨越幼儿园的AI制造课程课程，其中包括该计划开发的示例问题和工具。亚特兰大和盐湖城的高中教师以及来自代表性不足的少数民族人口的学生将获得这些课程的动手经验和教学。该研究维护和扩展了支持合金，陶瓷及其复合材料基础研究的强大计划；支持大学（佐治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）之间自由流动互动的方式；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。本研究计划使物理知识的人工智能（AI） - 驱动的金属基质复合材料的平行设计及其附加制造。 AI的概念可以同时发现并优化新材料及其增材制造（AM），并有望进一步彻底改变AM，但尚未实现。基础研究是为了使材料及其制造的自主AI发现和优化，以减少部署时间和成本，以满足材料基因组倡议的愿景。将填补五个关键数据驱动的算法差距：1）数据分析解释术算法，以从必不可少的过程结构 - 培训数据源中启用自动的，典型的数据策划。 2）自动化数据清洁和数据库串联的算法，以便当将新的数据源或数据特征纳入研究问题时，AI可以修改和附加数据空间。 3）自动化数据功能绘制跨多个长度和时间尺度的算法，以完成过程结构 - 培训数据本体。 4）数据功能工程算法可改善AI性能。 5）过程结构 - 培训机器学习模型，这些模型在多个嵌套子模型中学习全球关系。基于物理学的模型和实验将进行进步，以预测和验证其在发现和优化金属基质复合材料及其添加剂制造方面的实用性。高吞吐量一维，二维和三维表征数据分析将是自动化的。 Gotali合作伙伴Elementum 3D将提供一项技术经济基线研究，用于将新的金属矩阵复合材料商业化，以用于添加剂制造，以作为该计划所取得的进步的整体评估度量。新的AM测试工件的开发将使全球研究人员受益。 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）在计算机和信息科学与工程局（CISE）以及材料研究部（DMR）在数学和物理科学局（MPS）中（MPS）。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响审查Criteria，它被认为是值得通过评估的支持。