IUCRC Planning Grant University of Alabama in Huntsville: Center for Smart Manufacturing using AI-based Revolutionary Technologies (SMART)

About This Grant

This award funds planning activities for a proposed new Industry University Cooperative Research Center (IUCRC), the Center for Smart Manufacturing using AI-based Revolutionary Technologies (SMART). Advancements in machine learning (ML) and artificial intelligence (AI) have profoundly impacted numerous fields. However, the manufacturing sector has faced challenges in integrating AI at the same pace. The SMART center will leverage data collected through sensors and cameras during the manufacturing process to create and integrate AI technologies that transform the manufacturing sector, delivering enhanced productivity, product quality, factory sustainability, and workforce safety. The center aims to enhance the global strength and competitiveness of the U.S. manufacturing industry, which plays a critical role in economic stability and growth while sustaining technological leadership in key sectors including automotive, aerospace, electronics, and pharmaceuticals. The SMART center is also dedicated to workforce development through specialized training programs that will equip workers with the skillset essential for 21st-century industries. Through a collaborative approach involving universities, industry leaders, and government agencies, the SMART center aligns with national priorities to bolster economic resilience and advance technological innovation. The mission of the SMART center is to foster collaborations among stakeholders in advanced manufacturing to conduct and disseminate applied, pre-competitive research on AI-driven technologies, methodologies, and tools that enable the transformation of the manufacturing sector. The SMART center’s research focuses on four thrust areas: manufacturing productivity, product quality, factory sustainability, and workforce safety. By leveraging advanced AI and machine learning (ML) technologies, including deep learning, reinforcement learning, and large language models, these efforts aim to: (i) optimize various aspects of manufacturing processes, (ii) improve product quality through advanced defect detection systems utilizing analytics and deep neural networks, (iii) enhance resource sustainability by improving energy efficiency and reducing waste, and (iv) boost workplace safety through real-time monitoring and predictive analysis of potential hazards. The UAH site will specifically focus on the integration of ML with AI in the manufacturing sector and leverage Digital Twins (DT) to simulate the manufacturing environment. Camera-based imaging and sensors at UAH will be used to benchmark advancement of the ML/AL algorithms for seamless integration into manufacturing systems and ensure their reliability and efficiency. DT will replicate systems in industry to simulate equipment conditions in real-time, predict maintenance needs, and preemptively address potential failure. 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.