FMRG: Cyber: Manufacturing USA: Material-on-demand manufacturing through convergence of manufacturing, AI and materials science

FMRG：网络：美国制造：通过制造、人工智能和材料科学的融合实现按需制造材料

• 批准号: 2328395
• 负责人: Panganamala Kumar
• 金额: $ 300万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328395&HistoricalAwards=false
• 关键词: FMRG; Cyber; Manufacturing; USA; Material

Recent advances in AI are driving an industrial revolution, leading to the emergence of intelligent, autonomous systems. This Future CyberManufacturing research grant reimagines autonomy for a new generation of manufacturing machines, capable the manufacture of advanced alloy products with unprecedented performance affordably. The project brings together a diverse team of pioneers from academia and the industry in AI (including machine learning, adaptive control, and data science), materials science, and smart manufacturing, towards addressing the foundational research and skill development. The team includes Texas A&M University/Texas A&M Engineering Experiment Station, Brown University, Texas A&M University Kingsville, Prairie View A&M University, Houston Community College, and multiple industry, regional government, and academic partners. These foundations allow a new approach and demonstration platforms to harness recent advances in 3D printing, materials genomics, and sensor technologies to control the production processes and to mix multiple materials to obtain the desired properties. These products provide a critical competitive edge for the US economy and effective solutions for the national critical material challenges in the strategic hypersonic systems and energy conversion sectors. It will also provide students and industry professionals with opportunities for valuable education and skill development.The project tackles scientific challenges of realizing futuristic manufacturing machines endowed with a deep level of autonomy to make tailored materials-on-demand manufacturing. The autonomous manufacturing machine platforms are envisioned to generate process plans adaptively (fusing information from diverse data and knowledge sources) – to control material microstructure and composition beyond just geometry and morphology – to yield bulk-scale tailored material components with dramatically enhanced functional performance. The following four foundational contributions to autonomy principles would emerge from this effort: (1) Shape-constrained machine learning. The key idea in this novel form of physics-informed machine learning is to introduce constraints on the shape/sign of the underlying functional relationship to model incomplete physical and experiential knowledge. (2) Harness surprise observations. A surprise outcome from an experiment or a process has historically led to new discoveries and insights. Dealing with surprising observations differentiates an autonomous system from an automated one. (3) Safeguarding extrapolation using digital twins. The principles of fusing physical systems with multiple digital twins would be developed, each capturing certain physics with a specified fidelity. (4) Knowledge expansion. New approaches would be studied to capture experiential and deep knowledge in the public manufacturing literature/databases on process chains and the dynamic process-material relationships via innovative graph neural networks. These approaches will be validated to discover innovate new pathways to manufacture high-entropy alloys that retain strengths above 1400°C, demonstrating improved machinability and reduced use of expensive and scarce materials. The project would provide hands-on training and education, leveraging their expertise and collaborations with national Manufacturing USA, industry, and education networks.This Future Manufacturing research is supported by the Computer and Information Science and Engineering Directorate's Division of Computer and Network Systems (CISE/CNS), the Engineering Directorate's Division of Civil, Mechanical and Manufacturing Innovation (ENG/CMMI), the Engineering Directorate's Division Engineering Education and Centers (ENG/EEC), the Mathematical and Physical Sciences Directorate's Division of Mathematical Sciences (MPS/DMS), and the Technology, Innovation and Partnerships Directorate's Translational Impacts Division (TIP/TI).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.

人工智能的最新进展正在推动一场工业革命，导致智能、自主系统的出现。这项未来网络制造研究资助重新构想了新一代制造机器的自主性，能够以经济实惠的方式制造具有前所未有性能的先进合金产品。汇集了来自人工智能（包括机器学习、自适应控制和数据科学）、材料科学和智能制造领域的学术界和行业先驱的多元化团队，致力于解决基础研究和技能开发问题。该团队包括德克萨斯农工大学/。德克萨斯州A&M 工程实验站、布朗大学、德克萨斯 A&M 大学 Kingsville、Prairie View A&M 大学、休斯顿社区学院以及多个行业、地区政府和学术合作伙伴这些基金会提供了新的方法和演示平台，以利用 3D 打印、这些产品为美国经济提供了关键的竞争优势，并为战略高超音速系统和能源转换中的国家关键材料挑战提供了有效的解决方案。部门。还将为学生和行业专业人士提供宝贵的教育和技能发展机会。该项目旨在解决实现具有深度自主性的未来制造机器的科学挑战，以实现定制材料按需制造。设想的自主制造机器平台。自适应地生成工艺计划（融合来自不同数据和知识源的信息） - 控制材料的微观结构和成分，而不仅仅是几何形状和形态 - 产生具有显着增强的功能性能的批量定制材料组件 以下四个对自主原则的基本贡献。这项努力将会产生：（1）形状约束机器学习。这种新颖的物理信息机器学习形式的关键思想是引入对底层函数关系的形状/符号的约束，以对不完整的物理和经验知识进行建模（2）利用意外观察。历史上，实验或过程的结果会带来新的发现和见解，从而将自治系统与自动化系统区分开来。 (3) 使用数字孪生来保护外推法。 (4) 将研究新的方法，以通过创新的图神经网络捕获公共制造文献/数据库中有关过程链和动态过程-材料关系的经验和深层知识。这些方法将得到验证，以发现制造在 1400°C 以上仍保持强度的高熵合金的创新途径，展示出改进的机械加工性并减少昂贵和稀缺材料的使用。该项目将利用他们的优势提供实践培训和教育。专业知识以及与美国国家制造业、工业和教育网络的合作。这项未来制造业研究得到了计算机和信息科学与工程局计算机和网络系统部门 (CISE/CNS)、工程局土木、机械和工程部门的支持制造创新 (ENG/CMMI)、工程理事会工程教育和中心部 (ENG/EEC)、数学和物理科学理事会数学科学部 (MPS/DMS) 以及技术、创新与合作伙伴关系理事会的转化影响部门 (TIP/TI)。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。