Kelvin Chan
Papers
3
Total Citations
52
H-Index
3
About
Kelvin Chan is a specialist in robotic manufacturing and intelligent automation, with a particular focus on surface finishing processes such as grinding and polishing. His research addresses one of the most persistent challenges in advanced manufacturing: capturing and transferring the tacit knowledge of skilled human operators into programmable robotic systems. Chan's most significant contributions center on developing methodologies that bridge the gap between human expertise and robotic execution. Using instrumented, "sensorised" hand-held tools, his work captures critical process variables — including contact force, tool path, feed rate, and tool orientation — as skilled operators perform surface finishing tasks. These captured parameters are then translated into precise robotic programs, enabling machines to replicate nuanced human techniques with high fidelity. His 2016 paper on programming tool paths and orientations for robot belt grinding has garnered 27 citations, while earlier foundational work from 2014 establishing the core knowledge-capture methodology has attracted 17 citations. A complementary 2016 study on conformance grinding further demonstrates the breadth of his approach. Chan's research holds significant implications for industries requiring high-precision finishing, such as aerospace and medical device manufacturing, where consistent quality and reduced reliance on scarce skilled labor are paramount concerns.
Research Focus
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Top Papers
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