Prasanta Kumar Samal
National Institute of Engineering South Campus, T John Institute of Technology, JSS Science and Technology University, Veer Surendra Sai University of Technology, Mahindra and Mahindra Ltd
0000-0002-3688-259X
About
Prasanta Kumar Samal is an Assistant Professor in the Department of Mechanical Engineering at The National Institute of Engineering, India. He received his Master’s degree from Indian Institute of Science and completed his Ph.D. in Mechanical Engineering with research focused on vibration-based condition monitoring of anti-friction bearings using machine learning techniques.
Previously, he served as Deputy Manager in the Computer Aided Engineering (CAE) Center of Excellence at Mahindra Research Valley, the R&D hub of Mahindra & Mahindra, contributing to advanced simulation-driven product development. His research interests include structural health monitoring, intelligent condition monitoring of rotating machinery, vibration analysis, multibody dynamics, finite element modelling, and experimental modal analysis.
He is a Gold Medalist from Veer Surendra Sai University of Technology and secured AIR 26 in the Graduate Aptitude Test in Engineering (2009).
Employment
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National Institute of Engineering South Campus Assistant Professor2014 - Present
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T John Institute of Technology Assistant Professor2012 - 2014
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Mahindra and Mahindra Ltd Deputy Manger2011 - 2012
Education
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JSS Science and Technology University Ph. D.
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Veer Surendra Sai University of Technology B. Tech.
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Indian Institute of Science Bangalore ME
Projects & Funding
Projects & funding information is unavailable.
Publications (26)
- Evaluating the Efficacy of Machine Learning Models in Bearing Fault Classification Save
- Rotating bending fatigue analysis of 3D-printed PLA polymer composites: Effect of short fiber reinforcement and epoxy coating Save
- Machine Learning for Bearing Health Monitoring: A Controlled Experimental Benchmark Using Vibration-Based Statistical Features Save
- A Comparative Analysis of Machine Learning Algorithms for Fault Classification in Cylindrical Roller Bearings Save
- Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study Save
- AI-Enhanced Fault Diagnosis in Rolling Element Bearings: A Comprehensive Vibration Analysis Approach Save
- An Empirical Model Integrating Dimensional Analysis and Box-Behnken Design for Crack Detection in Rotor Fan Blades Save
- Evaluation of Damping Characteristics of 3D Printed CF-reinforced PLA/ABS Composite Sandwich Panels Save
- Evaluation of fatigue characteristics of 3D printed/composites reinforced with carbon fiber using design of experiments Save
- Fault Classification in Rolling Element Bearing Based on Vibration Signature Using Artificial Neural Network Save