Bio
Dr. Jaydeep Karandikar is a Senior R&D Staff in the Intelligent Machine Tool Research group, Manufacturing Science Division, at Oak Ridge National Laboratory. His broad research interests include machining process modeling, monitoring, & optimization, and smart manufacturing. Dr. Karandikar's projects at the Department of Energy's Manufacturing Demonstration Facility include physics-guided machine learning for milling stability modeling, goal-oriented active learning for process modeling and optimization, tool life modeling and monitoring in machining, and digital twins for machining. Dr. Karandikar has been a leader on technical collaborations with multiple industrial partners. Before joining ORNL, he was a lead research engineer at GE Research, Niskayuna, NY. Dr. Karandikar has published more than 25 peer-reviewed journal papers, two book chapters, and holds two US patents. Dr. Karandikar is a member of the executive committee of the Manufacturing Engineering Division at ASME and a Research Affiliate at CIRP.
Awards
Society of Manufacturing Engineers – SM WU Research Implementation Award, 2023
ORNL Innovation Award, 2021
Society of Manufacturing Engineers - Outstanding Young Manufacturing Engineer 2016
NAMRI/SME Outstanding Paper, 38th North American Manufacturing Research Conference, May 26, 2010.
Professional Service
Associate Editor, ASME Journal of Manufacturing Science and Engineering (2020 - 2026)
ASME Manufacturing Engineering Division (MED) Executive Committee Member (2022-2027, Chair 2026-2027)
CIRP Research Affiliate, 2021 - 2025
Trademarks and Patents
Karandikar, J., Unnikrishnan, J., Henderson, A. and Illouz, K., General Electric Co, 2020. Optimal machining parameter selection using a data-driven tool life modeling approach. U.S. Patent 10,564,624.
Karandikar, J., Mukundan, B. and Rangarajan, A., General Electric Co, 2020. Design tool for optimal part consolidation selection for additive manufacturing. U.S. Patent Application 16/425,443 (Pending)
Karandikar, J., Feldhausen, T., Saleeby, K., Smith, S., and Schmitz, T., UT Battelle LLC, 2022. Stability boundary and optimal stable parameter identification in machining. U.S. Patent Application 17/529,558.