Simon Mangold
Papers
4
Total Citations
81
H-Index
3
About
Simon Mangold is a leading researcher at the intersection of computer vision, robotics, and circular economy, with a primary focus on automating remanufacturing processes. His work addresses a critical bottleneck in sustainable manufacturing: the need to replace manual, labor-intensive disassembly with intelligent, autonomous systems. Mangold’s major contributions center on developing vision-based solutions for robotic disassembly, particularly for handling uncertain product conditions. His most cited paper, "Vision-Based Screw Head Detection for Automated Disassembly for Remanufacturing" (2022, 52 citations), provides a foundational method for identifying and manipulating threaded connections—a ubiquitous challenge in remanufacturing. He further advanced the field with pioneering work on sim2real transfer learning for point cloud segmentation (2023, 19 citations), enabling deep learning models trained in simulation to effectively operate on real-world industrial disassembly tasks. Mangold has also systematically investigated the physics of unscrewing, quantifying breakaway torque to inform robotic control strategies. His research is instrumental in creating agile production systems capable of adapting to unknown product states, directly supporting the economic viability of remanufacturing and the broader transition to a circular economy.
Research Focus
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Top Papers
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