Dr. Feng is a Group Leader of Materials Processing and Joining Group and a Distinguished R&D Staff of Oak Ridge National Laboratory. He manages a diverse R&D portfolio aimed at addressingthe materials processing and joining needs from automotive, aerospace, nuclear, petrochemical and power generation industries. His primary interest is in thermal-mechanical-metallurgical behaviors of materials during processing and joining. Most recent work included integrated computational welding engineering (ICWE), proactive weld residual stress control and management, friction stir welding and processing, characterization of weld by advanced neutron and synchrotron scattering, and novel solid-state joining processes of dissimilar metals. Dr. Feng received his Ph.D. in Welding Engineering from the Ohio State University. He is a Fellow of the American Welding Society, a Joint Faculty Professor¿Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, and Guest Professor of Tsinghua University. Dr. Feng has broad interactions with industry and extensive experience in solving critical industry [...]. Feng is currently one of Editors-in-Chief Transactions on Intelligent Welding Manufacturing (TIWM) authorized by Springer for periodical publication of research papers from 2017.

Dr. Jian Chen is a Research Staff in Materials Processing and Joining Group at Oak Ridge National Laboratory. He has significant experimental and analytical experiences in developing advanced materials joining and processing technologies and the associated control and monitoring techniques. His current R&D focuses on advanced welding and joining techniques, intelligent welding process monitoring and control, non-destructive weld quality inspection and high-performance welding simulation. Dr. Chen received his doctoral degree in Industrial Engineering from the Ohio State University. He is a member of American Welding Society's Technical Papers Committee and 2nd Vice Chair of American Welding Society's North East Tennessee Section.

Provides a comprehensive overview of non-destructive methods for detecting and reducing welding defects during gas tungsten arc welding (GTAW) based on computer vision and artificial intelligence

Presents research on visual sensing and control of GTAW

Discusses real-time welding quality control to reduce various welding defects