2026 ASEE Annual Conference & Exposition

Explainable AI-Driven Predictive Maintenance Curriculum for Smart Manufacturing

Presented at Manufacturing Division (MFG) Technical Session 5: Artificial Intelligence in Manufacturing Education: Impact, Implementation, and Future Directions

Nowadays, Industry 4.0 has transformed manufacturing industries into a data-rich system driven by IoT, automation, and Artificial Intelligence (AI). Within this context, Predictive Maintenance (PdM) provides a proactive strategy that leverages heterogeneous sensor data such as vibration, acoustic, electrical, and visual signals along with historical performance and advanced analytics to forecast equipment failures before they occur. Usually, AI-driven PdM (AI-PdM) enhances this capability by integrating AI-based sensor analytics to automate fault prediction and optimize system reliability. However, traditional AI-PdM often functions as a “black box,” providing limited interpretability of its decision-making process and posing challenges for trust, validation, and human oversight in critical manufacturing environments. To address these challenges, explainable AI-based PdM (XAI-PdM) extends beyond prediction accuracy by leveraging explainability through transparent-based analysis into intelligent maintenance decision-making. XAI-PdM-based framework integrates heterogeneous sensor data with interpretable AI models that not only predict equipment degradation but also clarify which sensors and features contribute to each prediction. Such transparency bridges the gaps between complex AI algorithms and engineering expertise, fostering trustworthy AI, traceability, and actionable insights for real-time maintenance. Identifying this need, it is essential to transfer XAI-PdM concepts to future educators, empowering teachers to prepare students for the demands of smart manufacturing environments. This paper provides condense guidelines for utilizing cutting-edge XAI tools with real industrial datasets, enabling students to enhance their problem-solving skills and workforce readiness for next-generation advanced manufacturing. The XAI-PdM-driven context integrates both theoretical and experiential learning to equip students with cross-disciplinary competencies in AI and data analytics. Ultimately, these guidelines provide to prepare students who can design, interpret, and deploy data-driven intelligent PdM systems that foster sustainability and innovations in the manufacturing industry.

Authors

Dr. Abdullah Al Mamun Old Dominion University [biography]

Dr. Abdullah Al Mamun is an Assistant Professor in the Department of Engineering Technology at Old Dominion University. He earned his Ph.D. in Industrial and Systems Engineering from Mississippi State University in August 2023, following the completion of his M.S. in Manufacturing Engineering at Minnesota State University in December 2015. Dr. Mamun’s research focuses on integrating advanced sensor technologies into smart manufacturing systems to enhance the cyber-physical resiliency of additive manufacturing, enable real-time machinery prognostics and anomaly detection, and optimize energy consumption for intelligent and sustainable production environments.
Dr. Murat Kuzlu http://orcid.org/https://0000-0002-8719-2353 Old Dominion University [biography]

Murat Kuzlu (Senior Member, IEEE) is an Associate Professor in the Department of Engineering Technology at Old Dominion University in Norfolk, VA, USA. He earned his B.Sc., M.Sc., and Ph.D. degrees in Electronics and Communication Engineering in 2001, 2004, and 2010, respectively. Before joining ODU, he served as a Senior Researcher at the Scientific and Technological Research Council of Turkey (TUBITAK) and as a Research Assistant Professor at the Advanced Research Institute of Virginia Tech. His research interests include smart systems, trustworthy AI, explainable AI, agentic AI, and next-generation networks.
Dr. Katherine Smith http://orcid.org/0000-0002-5026-4501 Old Dominion University [biography]

Katherine Smith received Ph.D. in Modeling and Simulation, M.S. in Applied and Computational Mathematics, and B.S. degrees in applied mathematics and mechanical engineering from Old Dominion University. Dr. Smith is an Assistant Professor in the Engineering Technology Department at Old Dominion University. Her research interests include data science, machine learning, and artificial intelligence, systems modeling and network analysis with applications in supply chain and cyber physical systems, data-driven implementation of industry 4.0 technologies, and serious game development.
Dr. Vukica M. Jovanovic http://orcid.org/0000-0002-8626-903X Old Dominion University [biography]

Dr. Vukica Jovanovic is the Chair of the Engineering Technology Department and a Full Professor of Engineering Technology. She holds a Ph.D. in Mechanical Engineering Technology from Purdue University, with a focus on Digital Manufacturing. In addition, she earned a Magister degree (equivalent to Ph.D. candidacy in the United States) in Industrial Engineering and Engineering Management, specializing in Production Systems Design, as well as a Dipl.-Ing. (Master’s) degree in Industrial Engineering and Engineering Management with a concentration in Mechatronics, Robotics, and Automation. Dr. Jovanovic brings extensive industry experience to academia. She worked as an Industrial Engineer in the manufacturing of aircraft wings and nacelles for commercial aircraft models ERJ-145 and ERJ-147 (Embraer) at Gamesa Aeronáutica in Vitoria/Bilbao, Spain. Prior to her university studies, she was a certified master electrician, completing apprenticeships in the power industry and an aluminum mill. Her professional foundation was further shaped through hands-on work in her family-owned manufacturing company, which specialized in sheet metal forming machines and metalworking services. Before pursuing an academic career, she served on the faculty at the University of Novi Sad in Serbia.
Dr. Dalya Ismael http://orcid.org/0009-0003-7410-3045 Old Dominion University [biography]

Dr. Dalya Ismael holds a Doctor of Philosophy degree in Civil & Environmental Engineering from Virginia Tech. She is currently an Assistant Professor of Civil Engineering Technology (CET) in the Department of Engineering Technology at Old Dominion University, where her research focuses on encouraging sustainable design and construction practices through behavioral interventions and visualization techniques. Dr. Ismael has more than nine years of teaching and industry experience, and is a LEED Green Associate and an Envision Specialist.
Adel El-Shahat Old Dominion University
Angela Sicaja Old Dominion University
Md Hedayetul Islam Chy Old Dominion University

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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