Papers
7
Total Citations
390
H-Index
6
About
Vigneashwara Pandiyan is a researcher specializing in intelligent manufacturing, robotic abrasive belt grinding, and machine learning-driven process monitoring. His work sits at the intersection of advanced manufacturing systems and artificial intelligence, with a particular focus on developing real-time, in-process monitoring solutions that eliminate costly offline inspection methods. Pandiyan's most influential contribution — garnering over 209 citations — introduced support vector machine and genetic algorithm techniques for tool condition monitoring in compliant abrasive belt grinding, establishing a foundational methodology in the field. He has since advanced this work by applying deep learning for virtual weld seam removal verification in robotic grinding systems (97 citations) and convolutional neural networks (CNN) for belt tool wear prediction using multi-axis force and vibration signatures. His early research on in-process surface roughness estimation through multi-sensor integration further demonstrated the practical viability of smart sensing in finishing operations. Across his body of work, Pandiyan consistently addresses challenges in aerospace-grade precision machining, where tight tolerances demand robust, adaptive monitoring. His more recent contributions explore distributed analytics frameworks for intelligent manufacturing architectures, reflecting a broadening vision toward Industry 4.0 integration. With cumulative citations approaching 400, his research offers valuable insights for engineers and researchers working on automation, smart sensing, and AI-driven quality control in manufacturing.
Research Focus
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