Uttam Kumar Mandal

About

Dr. Uttam Kumar Mandal is an Assistant Professor and HOD in the department of Production Engineering of the National Institute of Technology, Agartala. He has completed his B.Tech and M.Tech from Jadavpur University, Kolkata, West Bengal. He
is having 20 + years of teaching experience in Machining Technology, Engineering Mechanics, Strength of Materials, Fluid Mechanics, Machine Design, Mechanical Vibrations, Fundamentals of Heat Power Engineering, Machine Tools, Computer Integrated Manufacturing (CIM), AHP, QFD, Machines Tools, Decision Supporting System, Fluid control system & circuit. Behavioral Science, Production Investment Control, Simulation, and Their Applications, Reliability Control and Systems, Refrigeration and Air Conditioning, Computational Fluid Dynamics (CFD), Case-Based Reasoning (CBR), Intelligent Manufacturing System, Multi-criteria Decision Making (MCDM). He is also a reviewer of some of the well-known journals like International
Journal of Advanced Manufacturing Technology (Springer Publication), International Journal of Production Research (Taylor and Francis) to name a few. Above that, he is also having 2 years of industrial experience.

Employment

• National Institute of Technology Agartala HOD & Assistant Professor
  2018 - Present

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (29)

• A priority-based heuristic approach for solving flexible flow-shop with parallel machine scheduling in a fuzzy environment
  Granular Computing 2023 DOI: 10.1007/s41066-023-00377-y
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• Eco-Friendly Cutting Fluids in Minimum Quantity Lubrication Assisted Machining: A Review on the Perception of Sustainable Manufacturing
  International Journal of Precision Engineering and Manufacturing-Green Technology 2021 DOI: 10.1007/s40684-019-00158-6
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• RSM analysis of impact property and characterization of Al6063-Cu-TiO2-ZrO2 composites fabricated by stir casting process
  Sadhana - Academy Proceedings in Engineering Sciences 2021 DOI: 10.1007/s12046-021-01583-7
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• Swarm intelligence based selection of optimal end-milling parameters under minimum quantity nano-green lubricating environment
  Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 2021 DOI: 10.1177/09544062211012723
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• Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model
  Safety Science 2020 DOI: 10.1016/j.ssci.2020.104967
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• Novel Multi-objective Decision-Making and Trade-Off Approach for Selecting Optimal Machining Parameters of Inconel-800 Superalloy
  Arabian Journal for Science and Engineering 2020 DOI: 10.1007/s13369-020-04583-7
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• Synergistic effect of silica and pure palm oil on the machining performances of Inconel 690: A study for promoting minimum quantity nano doped-green lubricants
  Journal of Cleaner Production 2020 DOI: 10.1016/j.jclepro.2020.120755
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• Wear behaviour of TiAlN coated solid carbide end-mill under alumina enriched minimum quantity palm oil-based lubricating condition
  Tribology International 2020 DOI: 10.1016/j.triboint.2020.106310
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• Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS
  Neural Computing and Applications 2019 DOI: 10.1007/s00521-019-04450-z
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• VIKOR method for decision making problems in interval valued neutrosophic environment
  Studies in Fuzziness and Soft Computing 2019 DOI: 10.1007/978-3-030-00045-5_22
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