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Dr. Sumesh C. S.

Assistant Professor (Sl. Gd.), Department of Mechanical Engineering, School of Engineering, Coimbatore

Qualification: M.Tech., Ph.D
cs_sumesh@cb.amrita.edu
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Research Interest: Advanced Machining of Difficult-to-Machine Materials, Artificial Intelligence and Machine Learning for Manufacturing, Data-Driven Modeling and Explainable AI (XAI) for Smart Manufacturing

Bio Publications Teaching Membership Administration
Bio

Dr. Sumesh C. S. is an Assistant Professor (Selection Grade) in the Department of Mechanical Engineering at Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham. He joined the institution in August 2011 and has over fifteen years of experience in teaching, research, and academic administration. Throughout his academic career, he has remained committed to delivering quality engineering education, promoting research excellence, mentoring students, and contributing to institutional development through various academic and administrative responsibilities.

Dr. Sumesh obtained his Ph.D. in Mechanical Engineering with research focused on intelligent manufacturing and machining process optimization. His research interests include Artificial Intelligence and Machine Learning for manufacturing, intelligent machining systems, machining process modeling and optimization, tool condition monitoring, predictive analytics, and Explainable Artificial Intelligence (XAI). He is particularly interested in developing data-driven methodologies for predicting machining responses such as cutting force, surface roughness, and tool wear by integrating statistical techniques with advanced machine learning algorithms. His research aims to develop accurate, reliable, and interpretable predictive models that can enhance manufacturing productivity, product quality, and decision-making.

His recent research explores the application of machine learning algorithms such as Random Forest, Support Vector Regression (SVR), XGBoost, Decision Trees, K-Nearest Neighbors, and Linear Regression for machining process prediction and optimization. He has also worked extensively on data augmentation techniques, Gaussian Process Regression (GPR), feature importance analysis, and model validation to improve predictive performance, particularly when experimental datasets are limited. His work contributes to the advancement of intelligent manufacturing, smart machining, and Industry 4.0 by integrating conventional statistical methods with modern artificial intelligence techniques.

As an educator, Dr. Sumesh has taught a wide range of undergraduate and postgraduate courses in Mechanical Engineering, including manufacturing technology, machining processes, computer-aided manufacturing, and laboratory courses. He strongly believes in outcome-based education and continuously adopts innovative teaching methodologies to bridge the gap between theoretical concepts and industrial practice. He has guided numerous undergraduate and postgraduate student projects and encourages students to develop practical engineering solutions through research, innovation, and critical thinking.

To continuously enhance his professional expertise, Dr. Sumesh has actively participated in several faculty development programmes, workshops, certification courses, and technical training programmes. He successfully completed the AICTE-QIP PG Certificate Programme in Artificial Intelligence and Data Science offered by the Indian Institute of Information Technology Kottayam, securing Third Rank among all participants. This achievement further strengthened his knowledge in artificial intelligence, machine learning, and data analytics, enabling him to effectively integrate these emerging technologies into his teaching, research, and academic activities.

His research contributions include publications in reputed peer-reviewed journals and conference proceedings in the areas of machining, manufacturing systems, and artificial intelligence applications in engineering. His current research focuses on combining statistical experimental design, response surface methodology, machine learning, explainable AI, and predictive analytics to develop intelligent decision-support systems for advanced manufacturing processes. He is also interested in sustainable manufacturing, digital manufacturing, predictive maintenance, smart production systems, and the application of AI for solving real-world manufacturing challenges.

In addition to his academic responsibilities, Dr. Sumesh actively contributes to curriculum development, departmental activities, and institutional initiatives. He believes that engineering education should cultivate technical competence, ethical values, innovation, and lifelong learning among students. His long-term research vision is to develop intelligent, explainable, and sustainable manufacturing systems that improve industrial productivity, resource efficiency, and product quality through the effective integration of artificial intelligence and advanced data analytics. He continues to pursue collaborative research with academia and industry, striving to develop practical, innovative, and impactful solutions that contribute to the future of intelligent manufacturing.

Publications

Journal Article

Year : 2026

ANFIS–GA-based offline digital twin for surface roughness prediction in machining of cryogenically pre-cooled Inconel 718

Cite this Research Publication : C. S. Sumesh, Ajai Veeramani Meenakshi, Chakunta Shubham, Kochukaleecka Goutham Krishna, Pranav Manthirasalam, Sabarish Balajee, Mannepu Venkata Roshan, ANFIS–GA-based offline digital twin for surface roughness prediction in machining of cryogenically pre-cooled Inconel 718, International Journal on Interactive Design and Manufacturing (IJIDeM), Springer Science and Business Media LLC, 2026, https://doi.org/10.1007/s12008-026-02553-1

Publisher : Springer Science and Business Media LLC

Year : 2025

Multi-objective optimization of machining parameters in face milling of AISI 1045 steel to ensure operational continuity

Cite this Research Publication : C. S. Sumesh, M Venkata Roshan, Sajith S, Shravan R. N, Dinu Thomas Thekkuden, Multi-objective optimization of machining parameters in face milling of AISI 1045 steel to ensure operational continuity, Journal of Materials Science: Materials in Engineering, Springer Science and Business Media LLC, 2025, https://doi.org/10.1186/s40712-025-00362-0

Publisher : Springer Science and Business Media LLC

Year : 2024

Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach

Cite this Research Publication : M. Venkata Roshan, C. S. Sumesh, S. Sakthi Balaji, Maheet V. E. Manchi, M. Upendra Reddy, Abd Baghad, Sustainable Machining: A Case Study on Face Milling of AISI 1045 Steel Using a Multi-Objective Optimization Approach, International Journal on Interactive Design and Manufacturing (IJIDeM), Springer Science and Business Media LLC, 2024, https://doi.org/10.1007/s12008-024-02148-8

Publisher : Springer Science and Business Media LLC

Year : 2024

Optimizing Surface Roughness in Machining Ti6Al4V Alloys: a Comprehensive Study of Machining Parameters and Finite Element Method Validation

Cite this Research Publication : C. S. Sumesh, Ajith Ramesh, Optimizing Surface Roughness in Machining Ti6Al4V Alloys: a Comprehensive Study of Machining Parameters and Finite Element Method Validation, International Review of Mechanical Engineering (IREME), Praise Worthy Prize, 2024, https://doi.org/10.15866/ireme.v18i7.25258

Publisher : Praise Worthy Prize

Year : 2024

Optimization of Machining Parameters on Chip Thickness In Orthogonal Turning of AISI 1040 Steel

Cite this Research Publication : CS Sumesh, M Venkata Roshan, S Sakthi Balaji, M Upendra Reddy, Maheet VE Manchi, A Barathwaj, Abd Baghad, Optimization of Machining Parameters on Chip Thickness In Orthogonal Turning of AISI 1040 Steel, Academic Journal of Manufacturing Engineering,

Year : 2023

Empirical relationship for fracture energy in machining processes: a FEM-based investigation with AISI 1045 steel

Cite this Research Publication : Akash Jiyalal Damahe, C S Sumesh, Ajith Ramesh, Empirical relationship for fracture energy in machining processes: a FEM-based investigation with AISI 1045 steel, International Journal on Interactive Design and Manufacturing (IJIDeM), Springer Science and Business Media LLC, 2023, https://doi.org/10.1007/s12008-023-01596-y

Publisher : Springer Science and Business Media LLC

Year : 2022

Numerical Modelling and Optimization of Parameters for Extrusion of Ti and Mg Alloys

Cite this Research Publication : M Aravind, S Sriram, D Gajanand, Rohith Roshan BR, CS Sumesh, Numerical Modelling and Optimization of Parameters for Extrusion of Ti and Mg Alloys, Academic Journal of Manufacturing Engineering,2022.

Year : 2021

Optimization of Dimensional Tolerances and Material Removal Rate in the Orthogonal Turning of AISI 4340 Steel

Cite this Research Publication : Chathakudath Sukumaran Sumesh, Dawood Sheriff Akbar, Hari Shankar Purandharadass, Raghunandan J. Chandrasekaran, Optimization of Dimensional Tolerances and Material Removal Rate in the Orthogonal Turning of AISI 4340 Steel, Periodica Polytechnica Mechanical Engineering, Periodica Polytechnica Budapest University of Technology and Economics, 2021, https://doi.org/10.3311/ppme.16563

Publisher : Periodica Polytechnica Budapest University of Technology and Economics

Year : 2021

COMPARISON OF OPTIMIZATION TECHNIQUES FOR IMPROVING MACHINABILITY OF TI-6AL-4V ALLOY.

Cite this Research Publication : Nidhi Menon, Vamsi Krishna R, CS Sumesh, Ajith Ramesh, COMPARISON OF OPTIMIZATION TECHNIQUES FOR IMPROVING MACHINABILITY OF TI-6AL-4V ALLOY., Academic Journal of Manufacturing Engineering, 2021

Year : 2021

Numerical and Experimental Fracture Mechanics Based Optimisation of Specimen and Notch Parameters of Aa-5052 Alloy

Cite this Research Publication : CN Abishyam, Anurag Mishra, D Karthik, KK Kumar, CS Sumesh, Numerical and Experimental Fracture Mechanics Based Optimisation of Specimen and Notch Parameters of Aa-5052 Alloy, Academic Journal of Manufacturing Engineering, 2021.

Year : 2021

Finite element analysis and optimization of tube hydroforming process

Cite this Research Publication : Raut, S.V., Ramesh, A., Arun, A., Sumesh, C.S. Finite element analysis, and optimization of tube hydroforming process (2021) Materials Today: Proceedings, 46, pp. 5008-5016. DOI: 10.1016/j.matpr.2020.10.394

Year : 2020

Numerical modeling and optimization of cold rolling process of AA5086 sheets.

Cite this Research Publication : S. S. B. T., Babu, S., Sumesh C.S., and Dr. Ajith Ramesh, “Numerical modeling and optimization of cold rolling process of AA5086 sheets.”, Academic Journal of Manufacturing Engineering, vol. 18, no. 3, pp. 168-175, 2020.


Publisher : Academic Journal of Manufacturing Engineering

Year : 2020

Experimental investigation and optimization of surface grinding of spheroidal graphite cast iron using response surface methodology

Cite this Research Publication : Sumesh C.S., Harikrishna, S., Nair, H. S., Mahesh, V., and Ramkumar, R., “Experimental Investigation and Optimization of Surface Grinding of Spheroidal Graphite Cast Iron Using Response Surface Methodology”, Journal of Mechanical Engineering Research and Developments, vol. 43, no. 2, pp. 305-321, 2020.

Publisher : Journal of Mechanical Engineering Research and Developments

Year : 2019

Numerical modeling and multi objective optimization of face milling of AISI 304 steel

Cite this Research Publication : K. K., Sumesh C.S., and Dr. Ajith Ramesh, “Numerical modeling and multi objective optimization of face milling of AISI 304 steel”, Journal of Applied and Computational Mechanics, vol. 5, pp. 749-762, 2019.


Publisher : Journal of Applied and Computational Mechanics

Year : 2018

Numerical Modelling and Optimization of Surface Finish during Peripheral Milling of AISI 4340 Steel using RSM

Cite this Research Publication : P. Gopikrishnan, Akbar, A., Asokan, A., Bhaskar, B., and Sumesh C.S., “Numerical Modelling and Optimization of Surface Finish during Peripheral Milling of AISI 4340 Steel using RSM”, Materials Today: Proceedings, vol. 5, pp. 24612-24621, 2018.

Publisher : Materials Today: Proceedings

Year : 2018

Numerical modelling and optimization of dry orthogonal turning of Al6061 T6 alloy

Cite this Research Publication : Sumesh C.S. and Dr. Ajith Ramesh, “Numerical Modelling and Optimization of dry Orthogonal Turning of Al6061 T6 alloy”, Periodica Polytechnica Mechanical Engineering, vol. 62, pp. 196-202, 2018.

Publisher : Periodica Polytechnica Mechanical Engineering, Budapest University of Technology and Economics,

Year : 2018

Influence of notch depth-to-width ratios on J-integral and critical failure load of single-edge notched tensile aluminium 8011 alloy specimens

Cite this Research Publication : Sumesh C.S. and Narayanan, P. J. Arun, “Influence of Notch Depth-to-Width Ratios on J-Integral and Critical Failure Load of Single-Edge Notched Tensile Aluminium 8011 Alloy Specimens”, Journal of Solid Mechanics, vol. 10, pp. 86-97, 2018.

Publisher : Journal of Solid Mechanics

Year : 2015

Finite Element Modelling of Orthogonal Machining of Hard to Machine Materials

Cite this Research Publication : Dr. Ajith Ramesh, Sumesh C.S., Abhilash, P. M., and Rakesh, S., “Finite Element Modelling of Orthogonal Machining of Hard to Machine Materials”, International Journal of Machining and Machinability of Materials, vol. 17, pp. 543-568, 2015.


Publisher : International Journal of Machining and Machinability of Materials

Research Article

Year : 2025

Developing an offline digital twin framework for predicting surface roughness in machining of Ti6Al4V alloy

Cite this Research Publication : Sumesh C S, Ajith Ramesh, Developing an offline digital twin framework for predicting surface roughness in machining of Ti6Al4V alloy, Materials and Manufacturing Processes, Informa UK Limited, 2025, https://doi.org/10.1080/10426914.2025.2586496

Publisher : Informa UK Limited

Year : 2021

Experimental and numerical optimization of process parameters for thin wall machining of bearing housings

Cite this Research Publication : S. Umashankar, H.V. Manjunath, C.S. Sumesh, Experimental and numerical optimization of process parameters for thin wall machining of bearing housings, Materials Today: Proceedings, Elsevier BV, 2021, https://doi.org/10.1016/j.matpr.2020.10.336

Publisher : Elsevier BV

Conference Paper

Year : 2021

Numerical Modeling of Orthogonal Machining Process Using Smoothed Particle Hydrodynamics–-A Parametric Study

Cite this Research Publication : S. Babu Thekk Surendran, Sumesh C.S., and Dr. Ajith Ramesh, “Numerical Modeling of Orthogonal Machining Process Using Smoothed Particle Hydrodynamics–-A Parametric Study”, in Trends in Manufacturing and Engineering Management (Lecture Notes in Mechanical Engineering), Singapore, 2021.

Publisher : Trends in Manufacturing and Engineering Management (Lecture Notes in Mechanical Engineering),

Conference Proceedings

Year : 2015

Effect of Cutting Parameters on Feed Force in Machining AISI 1018 Steel

Cite this Research Publication : Sumesh C.S. and Sriram, K., “Effect of Cutting Parameters on Feed Force in Machining AISI 1018 Steel”, International Conference on Mechanical & Manufacturing Engineering. Trans Tech Publications, Sri Chandrasekharaendra Saraswathi Vishwa Maha Vidyalaya , Kanchipuram University, 2015.


Publisher : International Conference on Mechanical & Manufacturing Engineering. Trans Tech Publications

Teaching

Courses Taught

Engg. Mechanics, Engg. Drawing, CAD, Mechanics of Solids, Kinematics of Machines, Robot Dynamics, Robot Kinetics and Dynamics, etc

Membership
  • ISTE
  • TSI
Administration

Positions Held

  • Currently Held Positions: Academic Coordinator (ARE, M-Tech), Student Advisor (2026 Batch)
  • Previously Held Position: Batch Coordinator (2020 Batch), NBA File Coordinator
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