T.N. Deepu Kumar

About

Computational Scientist and Mechanical Engineer specializing in physics-informed modelling, intelligent manufacturing, and digital twin technologies. I hold a Ph.D. in Mechanical Engineering from IIT Madras and an M.Tech in Computational Mechanics from IIT Guwahati.

My research focuses on integrating first-principles engineering, multiphysics simulation, and AI/ML to develop reliable and interpretable digital twins for advanced manufacturing processes. I have contributed to areas including abrasive waterjet machining, machine tool thermal compensation, cyber-physical systems, and Physics-Informed Neural Networks (PINNs).

I am proficient in MATLAB, Python, and C/C++, with experience in numerical methods, process modelling, optimization, and industrial AI applications. I enjoy working in interdisciplinary and collaborative environments where engineering fundamentals and data-driven intelligence come together to solve complex manufacturing challenges.

Currently, I work as a Senior Engineer at TCS–General Motors, developing PINN-enabled digital twin solutions for smart manufacturing and process automation in the automotive domain.

Employment

• TCS-General Motors Senior Engineer
  2026 - Present
• Dayananda Sagar University Assistant Professor
  2025 - 2026
• IITM Pravartak Technologies Foundation Postdoctoral Fellow
  2024 - 2025
• IITM Pravartak Technologies Foundation Senior Project Associate
  2024 - 2024

Education

• IITM Pravartak Technologies Foundation (Research Park) Postdoctoral Fellow
  2024 - 2025
• Indian Institute of Technology Madras Ph.D.
  2019 - 2024

Projects & Funding

Projects & funding information is unavailable.

Publications (19)

• Modelling of Microchannel Cross-Sectional Profile Generated on Ti-6Al-4V Alloy by Micro-abrasive Waterjet
  2026 DOI: 10.1007/978-981-96-8369-7_20
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• A high-fidelity simulation with PINN-driven thermal reconstruction framework for real-time tool error prediction in air-cooled motorised spindles
  Measurement Science and Technology 2026 DOI: 10.1088/1361-6501/ae4bfa
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• An approach to predict the abrasive waterjet trepanned hole geometry by considering the through-kerf profile as a generatrix
  Measurement Journal of the International Measurement Confederation 2025 DOI: 10.1016/j.measurement.2025.118146
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• Enhancing abrasive waterjet trepanned hole geometry using a through-kerf prediction model informed sequential machining
  CIRP Journal of Manufacturing Science and Technology 2025 DOI: 10.1016/j.cirpj.2025.04.013
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• Shallow-angled jet impingement generated channel geometry prediction in milling Ti-6Al-4V alloy
  Journal of Manufacturing Processes 2025 DOI: 10.1016/j.jmapro.2024.12.047
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• High-Energy Beam-Based Surface Texturing on Advanced Engineering Materials Used in Bioimplants
  Nanomanufacturing Techniques in Sustainable Healthcare Applications 2024 DOI: 10.1201/9781003470311-8
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• Enablers for realizing controlled freeform surfaces by ultra-high-pressure waterjets for bioimplant manufacturing
  Bioimplants Manufacturing: Fundamentals and Advances 2024 DOI: 10.1201/9781003509943-11
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• Modelling framework to predict shallow-to-deep geometries of milled pockets by incorporating the effect of waterjet flow on nonplanar target
  International Journal of Advanced Manufacturing Technology 2024 DOI: 10.1007/s00170-024-14364-3
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• Integration of CFD simulated abrasive waterjet flow dynamics with the material removal model for kerf geometry prediction in overlapped erosion on Ti-6Al-4V alloy
  Simulation Modelling Practice and Theory 2023 DOI: 10.1016/j.simpat.2023.102788
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• Experimental investigation and modelling of the kerf profile in submerged milling by macro abrasive waterjet
  Journal of Manufacturing Science and Engineering 2023 DOI: 10.1115/1.4062547
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