Texas A&m Engineering Experiment Station

FMRG: Cyber: Manufacturing Usa: Material-on-demand Manufacturing Through Convergence of Manufacturing, Ai and Materials Science -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.