Motivation
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Based on the belief that "Manufacturing is a blend of various disciplines", my research motivation is a convergence of Mechanical Engineering, Polymer Engineering, and Digital Transformation.
At this point, my research is pursuing developing, designing, and inventing intelligent manufacturing technologies to solve challenges in the manufacturing industry. Current research focuses on AI-Based Autonomous Manufacturing (SW approach) for process monitoring, optimization, and control, Intelligent Process Control (HW approach) for manufacturing processes, quality and productivity improvement, and Quality Measurement for accelerating both SW and HW approaches. The main manufacturing scope of my research is Molding and Forming Multi-functional Polymer and Composite Materials and Structures for EV batteries, mobility applications including aerospace.
Looking forward to future collaborations.
Research Interests
AI-Based Autonomous Manufacturing
Manufacturing processes such as injection molding are a combination of various fields including material science (e.g., polymer rheology), process monitoring and control, and design of molds. Process setup relies on tacit knowledge and experiences because it is difficult to consider all effective factors in the process. This challenge is critical for countries with decreasing working-age populations like Korea. To address this challenge, I have proposed explainable AI or interpretable ML-based process data analysis (International Journal of Production Research, 2023; Polymers, 2021; KR Patent 10-2500376) as a key approach for autonomous process analysis and optimization. Based on this approach, I have proposed process data-centered and XAI-based process optimization (Journal of Manufacturing Systems, 2024). For practical application, I have expanded my approach to relocation of production sites such as reshoring or offshoring (Journal of Manufacturing Processes, 2023), reduction of physical data requirements for manufacturing AI/ML (Polymers, 2022; Journal of Intelligent Manufacturing, 2024), and automatic detection of surface defects such as gloss transition, flash, ejector marks, and other manufacturing processes including friction stir welding (Measurement, 2025; Applied Sciences, 2024), recently.
Intelligent Process Control
Intelligent process control equipment would be more important for autonomous manufacturing than AI-based technologies requiring large physical manufacturing data. Sophistically designed intelligent manufacturing equipment can deal with external disturbances such as batch-to-batch material property fluctuation, gradual performance decrease of manufacturing machines, or human error. I have proposed the in-process sensor-based automatic process control system (Journal of Manufacturing Processes, 2024), intelligent injection molding system (i-mold System, LG Electronics), and other novel concepts for manufacturing equipment (Patents). The main goal is to enable autonomous process control without human intervention or complicated/high-computational-power-required models.
Quality Measurement
Based on fundamental understandings of optical and appearance quality and defects, I have developed various automated quality measurement and defects detection methods including warpage measurement (Journal of Manufacturing Systems, 2024; Journal of Manufacturing Processes, 2023), birefringence of transparent injection-molded parts (Polymer Engineering and Science, 2022), gloss transition defects (Polymers, 2020; Korea-Australia Rheology Journal, 2021; Korea-Australia Rheology Journal, 2020), filling imbalance (International Journal of Precision Engineering and Manufacturing, 2015) as well as static and dynamic characteristics measurement of multi-layer composite structures (Proceedings of the Institution of Mechanical Engineering Part P-Journal of Sports Engineering and Technology, 2017).
Molding and Forming Multi-functional Polymer and Composite Materials and Structures
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