The development of equipment and technologies has led to advanced manufacturing processes, including additive manufacturing processes. The advanced manufacturing processes greatly impact various industries, such as the automotive, aerospace, and medical industries. However, one of the major challenges is how to ensure product quality by detecting and mitigating defects. For example, the voids and cracks of the aircraft materials cause severe problems in the aerospace industry. Therefore, quality assurance in the manufacturing processes has become a very important issue in providing consistently high-quality products. Considering the importance of this problem, these studies aim to accomplish quality assurance by developing advanced machine-learning approaches. In this work, several advanced machine learning methodologies using the domain knowledge from the process are proposed. These methods overcome some constraints and complex process dynamics of the actual process that degrade the performance of existing machine learning methodologies in achieving quality assurance. To validate the effectiveness of the proposed methodologies, various advanced manufacturing processes, including additive manufacturing and multistage assembly processes, are utilized. The performance of the proposed methodologies provides superior results for achieving quality assurance in various scenarios compared to existing state-of-the-art machine learning methods. The applications of the achievements in these studies are not limited to the manufacturing process. Therefore, the proposed machine learning approaches can be further extended to other application areas, such as healthcare systems. In addition, I will share my several on-going research in this area at the end of the seminar.

#Bio 

Jihoon Chung received the B.S. degree in industrial engineering from Hanyang University, Seoul, South Korea (2015), the M.S. degree in industrial and systems engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea (2017), and the Ph.D. degree in industrial and systems engineering from Virginia Tech, Blacksburg, VA, USA (2023). He is currently an Assistant Professor at the Department of Industrial Engineering, Hanyang University, Seoul, South Korea. His research interests include machine learning and data analytics in smart manufacturing.