UF SME (Society of Manufacturing Engineers) Seminar

Details

Title: AI-enabled next-generation high-performance in-situ monitoring and defect diagnosis in LPBF

Abstract: Sensing is a critical link for quality control in manufacturing. Laser powder bed fusion (LPBF) requires high spatial- and temporal-resolution multiphysics sensing to characterize, as the melt pool is small and disappears instantly. This seminar presents not only Dr. Rongxuan Wang’s and his team’s research on cutting-edge sensor development, such as ultra-high spatial-resolution 3D scanners and a thermal microscope, but also a new paradigm — AI-enabled sensors. Powered by AI, complex sensor raw signals can be decoded to reveal hidden information that traditional analytical models cannot identify, facilitating new scientific discoveries in laser-matter interactions and enabling advanced control.

Bio: Dr. Rongxuan Wang is a Breeden Fellow Assistant Professor at the Industrial and Systems Engineering at Auburn University. His current research focuses on advanced sensing, instrumentation, data analytics, and control in advanced manufacturing. Dr. Wang and his AMICS Group as earned multiple recognitions in the field, including the IISE manufacturing & design best track paper awards (2022), best student paper awards (2023, 2025), IISE Data Analytics and Information Systems Division Mobile/Web App Competition Winner (2023), and ASME/SME Student Manufacturing Design Competition finalists (2024, 2025), Robert J. Lyman Award from the Precast/Prestressed Concrete Institute (2022).
Dr. Wang actively engages with advanced manufacturing academic societies. He currently serves as a board member in the IISE M&D division and is one of the symposium organizers for the 2026 ASME International Manufacturing Science & Engineering Conference (MSEC).