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

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设想了新一代制造机器的自主性，能够制造具有前所未有的性能负担能力的先进合金产品。该项目汇集了来自学术界和人工智能（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行业的不同先驱团队，致力于基础研究和技能开发。该团队包括得克萨斯A M大学/得克萨斯A M工程实验站，布朗大学，得克萨斯A M大学金斯维尔，草原视图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，利用3D打印，材料基因组学和传感器技术的最新进展来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换领域的国家关键材料挑战提供了有效的解决方案。该项目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。该项目解决了实现未来制造机器的科学挑战，这些机器具有高度的自主性，可以按需定制材料制造。自主制造机器平台被设想为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-生产具有显著增强的功能性能的大规模定制材料组件。以下四个对自治原则的基本贡献将从这一努力中产生：（1）形状约束机器学习。这种新形式的物理信息机器学习的关键思想是对底层函数关系的形状/符号引入约束，以模拟不完整的物理和经验知识。(2)令人惊讶的观察。从历史上看，实验或过程的意外结果会导致新的发现和见解。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)使用数字孪生子保护外推。将物理系统与多个数字孪生体融合的原理将被开发出来，每个数字孪生体都以特定的保真度捕捉特定的物理学。(4)知识扩展。将研究新的方法，通过创新的图形神经网络，从公共制造业文献/数据库中获取关于工艺链和动态工艺-材料关系的经验和深入知识。这些方法将被验证，以发现创新的新途径来制造高熵合金，这些合金的强度保持在1400°C以上，从而提高了可加工性，减少了昂贵和稀缺材料的使用。该项目将提供实践培训和教育，利用他们的专业知识和与国家制造美国，工业和教育网络的合作。（CISE/CNS），工程理事会的土木，机械和制造创新部门（ENG/CMMI），工程理事会的工程教育和中心（ENG/EEC），数学和物理科学理事会的数学科学部（MPS/DMS），以及技术，创新与合作理事会的翻译影响部（TIP/TI）。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。

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.