A Manufacturing-Driven Approach to Advancing Community in Northeast Ohio
Lead PI:
Robert Gao

Smart and connected technologies have the potential to dramatically reshape the manufacturing landscape, affecting not only the way products are manufactured but also how the workforce is trained, providing new opportunities for employment and improving the economic viability and sustainability of communities where manufacturing companies reside. The Industrial Internet of Things (IIoT) infrastructure and integration of technologies from AI and Machine Learning is accelerating such changes, while capital investments to engage Small-to-Midsized-Manufacturers (SMMs) with IIoT and Industry 4.0 (I4.0) have remained a daunting task that impacts the economic viability of many communities. This project represents a collaborative effort among academic researchers in engineering, education, and management at two universities, educators in a local community college, community advocates and stakeholders, economic development partners, and local SMMs. The goal is to create a smart and connected community in Northeast Ohio by forging a strategic alliance between the community and SMMs that engage with IIoT technologies. Collectively, research and development efforts will be directed to transforming participating SMMs into 'smart' and connected enterprises with enhanced adaptivity, competitiveness, and resilience to new supply chain dynamics. This approach will enhance the competitiveness of the local SMM and at the same time prepare a workforce to meet the challenges of the introduced technology. Such transformation will position the community for economic growth through a 'smart and connected' local industrial base and upskilled workforce. The blueprint of this approach will also be transferrable to other communities.

The collaborative action plan consists of: (1) use-inspired innovative research, (2) education, workforce training and development, and (3) policy recommendations on community behavior and organizational development. Since effective transformation of manufacturing data to actionable information is key to successful implementation of I4.0 technologies, research on artificial intelligence (AI) for real-time operation monitoring and asset tracking will be conducted to improve quality control and productivity. Through process-embedded sensing, and edge- and cloud-computing, a customized learning and intervention platform will be created to facilitate digital transformation of the workforce from experience-based operation to data-guided optimization. Research on transfer learning will facilitate knowledge translation across machine equipment both within an SMM and to other SMMs across the organizational boundaries to create 'spill-over' learning effects that promote the creation of a collaborative SMM network that assists each other in times of need and opportunity. Collaborations between academia and community developers will create pathways for curriculum development and internships that facilitate workforce training and employment opportunities. Ultimately, the collective action plan will promote research on both fundamental and practical problems, and benefit education, workforce development, and economic advancement in both Northeast Ohio and other regions across the country where SMMs play a critical role in the wealth generation and wellbeing of the community.

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.

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: 10/01/2021 - 09/30/2024
Institution: Case Western Reserve University
Award Number: 2125460