Visakan Kadirkamanathan
Papers
3
Total Citations
65
H-Index
2
About
Visakan Kadirkamanathan is a leading researcher at the intersection of manufacturing engineering and machine learning, with a core focus on precision robotic machining and intelligent process control. His work addresses a critical challenge in modern manufacturing: overcoming the inherent flexibility and positioning errors of industrial robots to achieve the high precision required for aerospace and automotive applications. Kadirkamanathan’s major contributions lie in developing data-driven, learning-based frameworks for in-process error compensation and quality prediction. His highly cited 2021 paper (38 citations) pioneered a Gaussian process regression model using perturbation signals to predict part quality during robotic countersinking, enabling real-time corrections without post-process inspection. He further advanced this paradigm with a 2022 study (25 citations) introducing a two-step active learning approach for right-first-time manufacturing, dynamically compensating for robot errors to achieve dimensional accuracy. His most recent work (2026) extends these principles into next-generation learning-based error prediction systems. With cumulative citations exceeding 65 for his core manufacturing papers, Kadirkamanathan is recognized for transforming robotic machining from a trial-and-error process into a precise, intelligent, and autonomous system, significantly reducing waste and cycle times in high-stakes production environments.
Research Focus
Key Achievements
Top Papers
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