Won-Bin Oh

Papers

Total Citations

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About

Won-Bin Oh is a researcher specializing in robotic and automated welding systems, with a particular focus on Gas Metal Arc (GMA) welding processes. His work addresses some of the most pressing challenges in modern manufacturing automation, including real-time seam tracking, spatter detection, and welding quality optimization. Oh's most cited contribution, "A Study on Seam Tracking in Robotic GMA Welding Process" (2020, 11 citations), demonstrates his commitment to advancing the precision and reliability of robotic welding systems. Complementing this, his investigation into spatter tracking algorithms (2019, 6 citations) tackles one of GMA welding's most persistent drawbacks — spatter formation — by developing intelligent monitoring solutions that improve process control and weld integrity. His work on regression models for total bead area estimation (2019, 4 citations) further highlights his interest in data-driven approaches to welding quality assessment, enabling more accurate parameter optimization in automated production environments. Collectively, Oh's research reflects a systems-level thinking that bridges computer vision, machine learning, and welding engineering. His contributions are particularly valuable for industries transitioning from manual to fully automated welding, offering practical tools that enhance efficiency, precision, and quality control in high-volume manufacturing settings.