Defining Research and Education Challenges in IoT for Neighborhoods with Significant Numbers of Small-to-Mid-Sized Manufacturers
Lead PI:
Robert Gao

Smart and connected systems are changing communities, work, and the home profoundly. Automation over the past few decades has radically reshaped manufacturing, employment in manufacturing industries, and their local communities. The Internet of Things (IoT) will likely accelerate change, opening new opportunities and presenting new challenges. This grant brings together academic researchers in science, engineering, and business management at two universities, educators, community members and city leaders, and small-to-mid-sized manufacturers (SMM), all within a neighborhood of Cleveland, Ohio, to plan for research, education, and workforce development in their community.

This project develops action plans for research, education, and policy recommendations to benefit SMMs and the local community. The plans reflect an understanding of the investment in time and resources required to leverage IoT in order for the SMMs to transform themselves into "smart" manufacturers with increased productivity and energy efficiency to better capitalize on new markets. Within the local community, the plans reflect an understanding of the potential role of IoT in workforce education, recruitment, and retention. IoT has the potential to improve the quality of life of a community. This project brings these different perspectives together, creating a new language for engagement among stakeholders and creating new opportunities in a community for public-private investment in IoT-enabled technologies and applications. This project in a neighborhood of Ohio may serve as a model for small towns and communities to work and plan with their local universities and manufacturers to achieve the societal benefits from leveraging IoT technologies.

Robert Gao
Professor Gao's research interests are in the areas of signal transduction mechanisms for multi-physics sensing, mechatronic systems design, stochastic modeling, multi-resolution data analysis, and artificial intelligence/machine learning for improving the observability and control of manufacturing processes and product quality. His research integrates analytical, numerical, and experimental methods, and has led to the inventions of miniaturized sensors, high-speed measurement instruments, and AI-based data analytic methods to enhance in-situ monitoring and control of manufacturing processes (e.g., plastic injection molding, sheet metal stamping, microrolling, etc.) and prognosis of product quality and system performance (e.g., aircraft engines, building HVAC, batteries, etc.). His current research addresses AI-enhanced control, intelligence, and autonomy of hybrid autonomous manufacturing processes (e.g., incremental forming and additive manufacturing), which is part of the recently established NSF Engineering Research Center on Hybrid Autonomous Manufacturing: Moving from Evolution to Revolution (NSF ERC HAMMER). He has published three books, over 400 technical papers (including 200 journal articles), 13 awarded patents, and given more than 120 invited talks.
Performance Period: 08/15/2017 - 12/31/2018
Institution: Case Western Reserve University
Award Number: 1737612