Chun-Chien Ting

Papers

Total Citations

H-Index

About

Chun-Chien Ting is a researcher focused on advancing precision manufacturing and robotic automation, with particular expertise in 3D deburring processes and trajectory optimization. His most-cited work, "The 3D Deburring Processing Trajectory Recognition Method and Its Application Base on Random Sample Consensus" (2022), addresses a critical bottleneck in automated manufacturing: the suboptimal trajectories generated by conventional offline programming methods. By applying the Random Sample Consensus (RANSAC) algorithm to 3D deburring, Ting developed a novel trajectory recognition method that significantly enhances robotic arm precision, reducing dimensional errors in workpieces. This contribution directly improves the efficiency and accuracy of automated deburring systems, a key process in industries like aerospace and automotive manufacturing. While his citation count of 2 reflects the recency of his work, the practical implications of his research—bridging the gap between theoretical trajectory planning and real-world robotic precision—demonstrate his potential for future impact. Ting’s work stands out for its targeted solution to a persistent industrial challenge, positioning him as an emerging voice in the intersection of computer vision, robotics, and manufacturing quality control.