Daegwon Koh

Gwangju Institute of Science and Technology

Papers

Total Citations

H-Index

About

Daegwon Koh is a leading researcher in advanced manufacturing and machine vision, with a focus on automating precision surface inspection for industrial applications. His most cited work, "Image-Based Inspection Technique of a Machined Metal Surface for an Unmanned Lapping Process" (2019, 29 citations), introduces a novel machine vision framework that enables the automated classification of surface textures on medium- and large-sized mold products used in automotive, television, and refrigerator manufacturing. This contribution directly addresses the limitations of traditional, labor-intensive inspection methods that rely on skilled workers, replacing them with an efficient, unmanned system. By integrating image processing and pattern recognition, Koh’s research significantly enhances quality control in high-precision lapping processes, reducing human error and operational costs. His work is pivotal for advancing smart manufacturing and Industry 4.0 initiatives, where real-time, data-driven inspection is critical. With growing citation impact, Koh’s innovations are shaping the future of automated surface metrology, offering practical solutions for industries demanding consistent, high-quality finishes. His dedication to bridging computer vision and mechanical engineering makes him a notable figure in modern manufacturing research.