Physics-Guided Machine Learning for Self-Aware Machining

Noel P. Greis,1 Monica L. Nogueira,2 Sambit Bhattacharya,3 Tony Schmitz4

North Carolina State University,1,2 Fayetteville State University,3 University of Tennessee Knoxville4 npgreis@ncsu.edu,1 mdnoguei@ncsu.edu, 2 sbhattac@uncfsu.edu 3 tony.schmitz@utk.edu

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The Future of Intelligent Manufacturing

Nearly one half million unfilled manufacturing positions (Deloitt), low productivity and strong competition for new products are driving significant need for increasingly intelligent, agile, and collaborative manufacturing. Significant advanced manufacturing activities are underway across the world from global manufactures in a multiplicity of verticals including pharmaceuticals, medical products, automotive, aerospace, consumer goods, construction, power and hand tools, materials, and industrials. Intelligent machines are addressing an increasingly complex range of manufacturing tasks including product design, material handling, machine tending, assembly, packaging, and distribution. Intelligent systems can provide a range of potential benefits to include speed, flexibility, accuracy. The purpose of this Spring Symposium is to bring together leading experts from across diverse communities to craft a 10-year road map for AI in manufacturing.

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