- Ph.D., Materials Science and Engineering, Carnegie Mellon University, 2017
- M.S., Materials Science and Engineering, Carnegie Mellon University, 2013
- B.S., Materials Science and Engineering, Beihang University, 2012
Dr. Tang specializes in mechanical metallurgy, with an emphasis on defect characterization, root cause analysis, and life prediction. He has 10-years research experience in metals, including aluminum, iron & steel, titanium, and nickel. He has expertise in the application of microscopy (automated SEM) techniques in various areas, particularly quantifying defects (pores and non-metallic inclusions) for a broad range of metallic materials and examining their effect on mechanical performance.
Dr. Tang has also developed a strong background in data science, using programming (python), machine learning (scikit-learn), and artificial intelligence (TensorFlow) to solve practical industrial problems.
Prior to joining Exponent, Dr. Tang worked as a Senior Research Engineer at ArcelorMittal Global R&D center, where he led multiple steelmaking projects related to defect quantification and process optimization. He managed the steel cleanliness characterization facility in support of product development, root cause analysis, and quality evaluation. He led projects on R&D fundamental steelmaking research to sustain new high-strength steel product manufacturability for automotive customers, such as using AI-driven accelerated defect analysis for energy-efficient steelmaking.
Before joining ArcelorMittal, Dr. Tang completed his graduate research at Carnegie Mellon University, in the Department of Materials Science and Engineering. His doctoral research focuses on the aluminum-silicon alloy parts produced by additive manufacturing (specifically, selective laser melting), including defect characterization (by 2D SEM and 3D CT), microstructure control, and fatigue life prediction. He was also a member of the Next Manufacturing Center (Carnegie Mellon University's research center for metal additive manufacturing).