Sung Dae Choi

Papers

Total Citations

H-Index

About

Sung Dae Choi is a researcher specializing in manufacturing automation, robotics, and precision engineering, with a particular focus on optimizing offline programming (OLP) for industrial robot systems. His major contributions center on enhancing the accuracy and efficiency of automated grinding and lapping processes—critical final stages in mold manufacturing that traditionally rely on manual labor. In his most-cited work, Choi analyzed automatic teaching position errors using laser tracker technology to improve OLP for grinding and lapping robots, addressing the bottleneck that manual operations create in the overall mold production workflow. By quantifying and correcting these positional inaccuracies, his research enables more reliable automation, reducing human dependency and improving mold quality consistency. While his citation count (2) reflects a niche but specialized impact, his work holds practical significance for industries seeking to automate high-precision finishing tasks. Choi’s contributions exemplify how metrology and robotics integration can solve real-world manufacturing challenges, offering valuable insights for researchers and engineers advancing smart factory and precision automation systems.