RII Track-2 FEC: Explainable and Adaptable Artificial Intelligence for Advanced Manufacturing

Advanced technologies have radically transformed manufacturing and are essential to modern economic prosperity. The goal of this project is to leverage emerging technologies, i.e., artificial intelligence (AI), 3D metal printing, and robotics, to increase the quality, capability, safety, and sustainability of Advanced Manufacturing (AdvMfg) in northern New England.

The project will also encourage the adoption of new technologies in industry to address manufacturing challenges facing the region. These two objectives will be accomplished by creating a scientifically- and geographically-interlinked team, i.e., Northeast Integrated Intelligent Manufacturing Lab (NIIM), consisting of members from the University of Maine, University of New Hampshire, University of Vermont, Dartmouth College, Southern Maine Community College, and Vermont Technical College communities.

Although initial funding for NIIM is from a National Science Foundation (NSF) Research Infrastructure Improvement Track-2 Focused EPSCoR Collaboration (RII Track-2 FEC) award, NIIM will sustainably impact the EPSCoR jurisdictions of Maine (ME), New Hampshire (NH), and Vermont (VT) for years to come. NIIM will draw on the unique strengths and rich assets of each state, and fully leverage existing state and federal investments.

The project's research team, led by early career faculty and senior mentors, will investigate how to integrate state-of-the-art AI techniques into modern manufacturing processes and systems. A proactive large-scale workforce and economic development assessment will identify the technological needs of firms in the region, which will inform project research and outreach activities, as well as identify skills gaps and opportunities for training and building career pathways in AdvMfg.

The project will extend STEM experiences to undergraduates and graduates, especially those underrepresented in STEM fields. This project will also create new components for Upward Bound for low-income high school students, who will potentially be first-generation undergraduate students, and Northeast Passage for disabled students and workers at community and technical colleges.

The team will work closely with the manufacturing extension partnership programs (MEPs) in the three states, an industrial advisory board, industry partners, and the US Economic Development Administration University Center for Economic Development. Working with these organizations will ensure that this Track-2 project remains closely tied to state and regional economic development priorities.

In this era of Industry 4.0, intelligent tools and techniques are opening new dimensions to optimize manufacturing processes and systems. The Northeast Integrated Intelligent Manufacturing Lab (NIIM), established in this project, aims to create a new, explainable and adaptable AI framework that fills existing and future technology gaps in manufacturing, such as long and expensive experiments and simulations, lack of coordination among multiple machines, and difficulty in programming robots for complicated manufacturing tasks.

Our convergent research teams across three EPSCoR jurisdictions (ME, NH and VT) will work closely with industry to create: (a) new AI models with intrinsic interpretability and increased adaptability to support Advanced Manufacturing (AdvMfg); (b) AI-guided design for additive manufacturing of metals that seamlessly connects multi-scale modeling and property predictions without unnecessary trial-and-error; (c) self-aware CNC machines that optimize the coordination and control in subtractive manufacturing; (d) industrial robots that efficiently and safely learn from video demonstrations for cellular manufacturing; (e) an industry-driven, unified hybrid manufacturing framework; and (f) an understanding of the factors that influence the adoption of new technologies by manufacturing businesses. The project anticipates specific outcomes that will be of immediate relevance to AdvMfg companies in Northern New England.

For example, it is expected that the project will yield sample-efficient robot learning techniques that will enable factory workers to teach robots new skills through visual demonstrations, allow robots to learn from failure and request relevant demonstrations, and generate risk-bounded safe policies using uncertainty aware learning. This project will serve the northern New England manufacturing sector through relevant research, workforce development, and education.

Diversity and inclusion efforts are integrated to remove barriers to STEM education for underrepresented, low income, potential first-generation, and/or disabled individuals.

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.