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.

AI的最新进展正在推动工业革命，导致智能，自主系统的出现。这项未来的网络制造研究赠款重新构想了新一代制造机的自主权，该机器能够适当地具有前所未有的性能的高级合金产品。该项目汇集了来自学术界和人工智能行业（包括机器学习，适应性控制和数据科学），材料科学和智能制造的潜水者团队，以解决基础研究和技能开发。该团队包括德克萨斯A＆M大学/德克萨斯州A＆M工程实验站，布朗大学，得克萨斯州A＆M大学，金斯维尔大学，草原景观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。这些基础允许一种新的方法和演示平台来利用3D打印，材料基因组学和传感器技术的最新进展，以控制生产过程并混合多种材料以获得所需的特性。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化部门的国家关键物质挑战提供了有效的解决方案。它还将为学生和行业专业人员提供价值教育和技能发展的机会。该项目应对实现未来派制造机的科学挑战，并具有深厚的自治水平，以制造按需按需制造的量身定制的材料。设想自动制造机器平台可以自适应地生成过程计划（从潜水员数据和知识源中融合信息），以控制材料微观结构和组成，而不仅仅是几何学和形态，以产生庞大的量身定制的材料组件，并具有巨大的功能性能。以下四项对自治原则的基础贡献将从这项工作中得出：（1）形状受限的机器学习。这种新颖形式的物理信息机器学习形式的关键思想是对基础功能关系的形状/迹象引入模型不完整的物理和专家知识的限制。 （2）线束令人惊讶的观察。从历史上看，实验或过程的令人惊讶的结果导致了新的发现和见解。处理令人惊讶的观察结果将自主系统与自动系统区分开来。 （3）使用数字双胞胎保护外推。将开发将物理系统与多个数字双胞胎融合的原理，每个人都以指定的保真度捕获某些物理。 （4）知识扩展。将研究新的方法，以捕获有关过程链和通过创新的图形中性网络的过程链和动态过程关系关系的经验和深入知识。这些方法将得到验证，以发现创新的新途径，以生产高超过1400°C以上的优势，表明加工的加工并减少了昂贵且稀缺的材料的使用。该项目将提供动手培训和教育，利用其与国家制造业，行业和教育网络的专业知识和合作。这项未来的制造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（CISE/CNS）的支持数学和物理科学局的数学科学部（MPS/DMS）以及技术，创新和合作伙伴局的翻译影响部门（TIP/TI）。这项奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估Criteria criteria criteria criteria。