Rongxuan (Raphael) Wang
Assistant Professor
Dr. Rongxuan Wang is a Breeden Fellow Assistant Professor at the Industrial and Systems Engineering at Auburn University. Dr. Wang’s academic journey includes a bachelor’s degree in mechanical engineering from Purdue University (2015), a master’s degree in industrial and systems engineering from Virginia Tech (2019), and a doctoral degree in industrial and systems engineering from Virginia Tech (May 2023).
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 IISE M&D division and is one of the symposium organizers for 2026 ASME International Manufacturing Science & Engineering Conference (MSEC).
Major Publications:
In situ investigation into temperature evolution and heat generation during additive friction stir deposition: A comparative study of Cu and Al-Mg-Si
D Garcia, WD Hartley, HA Rauch, RJ Griffiths, R Wang, ZJ Kong, Y Zhu, …
Additive Manufacturing 34, 101386
Development of structured light 3D-scanner with high spatial resolution and its applications for additive manufacturing quality assurance
R Wang, AC Law, D Garcia, S Yang, Z Kong
The International Journal of Advanced Manufacturing Technology 117
Toward online layer-wise surface morphology measurement in additive manufacturing using a deep learning-based approach
C Liu, RR Wang, I Ho, ZJ Kong, C Williams, S Babu, C Joslin
Journal of Intelligent Manufacturing 34 (6), 2673-2689
Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform
R Wang, B Standfield, C Dou, AC Law, ZJ Kong
Additive Manufacturing 66, 103449
In situ melt pool measurements for laser powder bed fusion using multi sensing and correlation analysis
Rongxuan Wang, David Garcia, Rakesh R Kamath, Chaoran Dou, Xiaohan Ma, Bo Shen, Hahn Choo, Kamel Fezzaa, Hang Z Yu, Zhenyu Kong
Scientific reports 12 (1), 13716
Sub-surface thermal measurement in additive manufacturing via machine learning-enabled high-resolution fiber optic sensing
R Wang, R Wang, C Dou, S Yang, R Gnanasambandam, A Wang, Z Kong
Nature Communications 15 (1), 7568