Thomas Koninckx
Papers
1
Total Citations
2
H-Index
1
About
Thomas Koninckx is a researcher whose work lies at the intersection of 3D scanning, computer vision, and automated manufacturing quality control. His key contributions center on developing intelligent, adaptive methods for detecting surface irregularities—specifically burrs—on manufactured parts, a critical challenge in precision engineering. His most cited work, "Automatic Burr Detection on Surfaces of Revolution Based on Adaptive 3D Scanning" (2005), introduces a novel approach that uses structured light ranging to acquire partial 3D scans of workpieces, then automatically identifies the presence and location of geometrical defects. This research has garnered 2 citations, reflecting its specialized niche within the field of automated inspection. Koninckx’s work is notable for bridging the gap between theoretical computer vision algorithms and practical industrial applications, offering a pathway toward more efficient, automated deburring processes. His contributions are particularly valuable for students and researchers interested in the intersection of 3D sensing, surface metrology, and manufacturing automation, demonstrating how adaptive scanning techniques can solve real-world quality control problems.
Research Focus
Key Achievements
Top Papers
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