Manufacturing Engineering: Machining and Coating Technology

Manufacturing Engineering is a critical pillar of modern industrial development, focusing on the creation of high-quality, precision-engineered products through innovative processes and advanced material handling techniques. At the Academy of Technology (AOT), research in Machining and Coating Technology blends traditional engineering expertise with state-of-the-art innovations to address industry needs for efficiency, accuracy, durability, and sustainability.

Machining processes — such as turning, milling, drilling, and grinding — remain fundamental to producing components with precise dimensions and surface finishes. However, the demands of modern industries like aerospace, automotive, biomedical devices, and renewable energy require machining techniques that are not only precise but also energy-efficient, cost-effective, and adaptable to complex materials. Researchers at AOT explore advanced machining methods including high-speed machining, micro-machining, and CNC-based adaptive manufacturing. By integrating automation, real-time monitoring, and optimization algorithms, these methods enhance productivity, reduce waste, and improve process reliability.

Alongside machining, coating technology plays a vital role in extending the service life and performance of manufactured components. Coatings provide protective barriers against wear, corrosion, heat, and chemical degradation. At AOT, research in this area focuses on advanced surface engineering techniques such as physical vapor deposition (PVD), chemical vapor deposition (CVD), plasma spraying, and thermal barrier coatings. These methods are used to enhance the properties of cutting tools, turbine blades, medical implants, and other high-performance parts. By tailoring coating composition and microstructure, researchers achieve improvements in hardness, friction reduction, thermal stability, and resistance to extreme environments.

An important direction of AOT’s research is the integration of machining and coating innovations for synergistic performance gains. For example, optimized machining of substrates ensures better adhesion and uniformity of coatings, while advanced coatings allow tools to operate at higher speeds and temperatures without premature wear. This combination results in superior product quality, longer tool life, and reduced downtime in manufacturing systems.

Applications of AOT’s research extend across multiple industries. In aerospace, precision machining and high-temperature coatings contribute to lighter, more fuel-efficient aircraft. In automotive engineering, improved machining tolerances and wear-resistant coatings enable the production of high-performance engines with reduced maintenance needs. In the biomedical sector, custom-machined implants with biocompatible coatings enhance patient outcomes.

The research is supported by well-equipped laboratories at AOT, featuring CNC machining centers, surface characterization tools, and coating deposition systems. Faculty members collaborate with manufacturing companies, research organizations, and material science experts to ensure that solutions are industry-relevant and globally competitive. Students are actively involved in research projects, gaining exposure to practical problem-solving, experimental analysis, and advanced manufacturing software tools.

Looking ahead, AOT’s work in Machining and Coating Technology will explore hybrid manufacturing systems that combine additive and subtractive processes, environmentally friendly coating materials, and AI-driven process optimization. The goal is to create manufacturing solutions that are not only technically superior but also sustainable, supporting the global shift towards greener and more efficient production systems.

Through this research, the Academy of Technology continues to contribute to the advancement of manufacturing science, preparing engineers to meet the challenges of Industry 4.0 with innovation, precision, and responsibility.

Researchers:

Dr. Jhumpa De (ME Department)

Projects:

Traditional and non-traditional Machining of INCONEL 718, Characterization and parametric optimization
Coating technology: Electroless and Electrolytic coating processes

Problem Description: Inconel 718 super alloy has been machined using HMT NH22 lathe without the application of cutting fluid. The cutting velocity, feed and depth of cut have been varied to obtain a dataset using Central Composite Design of experiment. The Roughness (Ra) of the samples were measured and predictive models were developed using regression and Response surface methodology. The optimum values of the process parameters were obtained considering minimization of the surface roughness subject to the ranges of the process parameters using Genetic Algorithm.

In non-traditional machining of Inconel 718, Titanium and graphite powder mixed electric discharge machining (PMEDM) was developed to improve the material removal rate and surface roughness. In this study, PMEDM was performed on Inconel 718 by adding titanium particles to the dielectric fluid. The input parameters selected for the experiment were powder concentration, pulse current, gap voltage, pulse on time and pulse off time and the effects of the input parameters were investigated on the material removal rate (MRR) and the surface roughness of the sample. Central Composite Design of Experiment was considered to prepare the samples varying the process parameters. Mamdani based fuzzy logic was developed using the experimental data and used to predict optimized machining conditions.

Focus of Research:

Super alloys are difficult to machine. The surface roughness is reduced without using conventional cutting fluids to reduce environmental pollution.
To study the effect of Titanium and graphite powder during Electro discharge machining of Inconel 718.

Publications:

S. Bhowmick, R. Mondal, S. Sarkar, N. Biswas, J. De and G. Majumdar, Parametric optimization and prediction of MRR and surface roughness of titanium mixed EDM for Inconel 718 using RSM and fuzzy logic, CIRP Journal of Manufacturing Science and Technology, vol.40, pp.10-28, February, 2023. https://doi.org/10.1016/j.cirpj.2022.11.002

H. Kedia, A. Pandey, A. Kumar, A. Majumdar, J. De, and N. Ghosh, Optimization of process parameters in machining of Inconel 718 super alloy on HMT (NH22) capstan lathe using genetic algorithm subject to minimization of surface roughness, Materials Today: Proceedings, July, 2022. https://doi.org/10.1016/j.matpr.2022.06.390
S. Bhowmick, A. Paul, N. Biswas, J. De, S. Sarkar, and G. Majumdar, Synthesis and Characterization of Titanium and Graphite Powder Mixed Electric Discharge Machining on Inconel 718, Advanced Production and Industrial Engineering, pp. 58-63, IOS Press,2022. https:// 10.3233/ATDE220722

Student Projects:

Title: Optimization of process parameters in machining of Inconel 718 super alloy on HMT NH22 lathe using genetic algorithm subject to minimization of surface roughness

Students: Harsita Kedia, Ashutosh Pandey, Ankit Kumar, Amit Majumder

Supervisors: Dr. Jhumpa De and Prof. Niloy Ghosh (ME Department)

Year: 2023

Problem Description: Electroless Nickel Phosphorous based binary, ternary alloy coatings and composite coatings with uniform dispersion of Carbon nano tubes were deposited from an aqueous solution of Nickel salt and reducing agents. The different mechanical, electro-chemical and tribological properties were studied by varying the condition of deposition and post heat treatment also. The predictive models of the process were developed using statistical and machine learning approaches. Afterwards, the optimal values of the process parameters were identified to improve the properties of the coated substrates using Taguchi, Response Surface methodology and soft computing approaches.

Electrolytic Nickel coating was deposited onto copper substrate using an acidic aqueous solution of Nickel salt. The Nickel strip was used as anode and copper strip was used as cathode. The different mechanical and physical properties were studied by varying the condition of deposition. The predictive models of the process were developed using statistical and machine learning approaches. Afterwards, the optimal values of the process parameters were identified to improve the properties of the coated substrates using Taguchi, Grey Taguchi and soft computing approaches.

Focus of Research:

The coatings are deposited to improve the properties of the substrate.
The mathematical models were developed using both statistical and Machine Learning approaches to predict different properties by varying the values of the process parameters.
The optimum values of the process parameters were evaluated by minimizing or maximizing the predictive models subject to the ranges of the process parameters.

Publications:

Jhumpa De, Ambikesh Kumar Srivastwa and Tarun Kumar Tiwary, Improving the deposition of electroless Nickel-Copper-Phosphorous coating using Non Dominant Sorting Genetic Algorithm-II, Sādhanā 50.3 (2025): 158. https://doi.org/10.1007/s12046-025-02812-z
Mandal, Rupam, Anamitra Ghosh, Subhasish Sarkar, Jhumpa De, Tapendu Mandal, Rajat Subhra Sen, and Gautam Majumdar, Parametric Optimization to Maximize Microhardness of Electroless Ni-Sn-P Coating Using Taguchi and Evolutionary Approaches, Journal of The Institution of Engineers (India): Series C 105, no. 5 (2024): 1217-1231. https://doi.org/10.1007/s40032-024-01094-4
Niloy Ghosh, Jhumpa De, and Amit Roy Chowdhury, Efficacy improvement technique of air-filtration unit affected by biofouling using electroless Ni-Cu-P coating, Indian Journal of Chemical Technology, Vol. 31, pp. 702-709, 2024, https://doi.org/10.56042/ijct.v31i5.10898
Jhumpa De, S. Sarkar, P. Roy, R. S. Sen, G. Majumdar, and B. Oraon, Synthesis and characterization of electroless Ni-Co-P coating, Sādhanā, vol. 47, no.3, pp.1-12, August, 2022. https://doi.org/10.1007/s12046-022-01933-z
A.K. Srivastwa, S. Sarkar, Jhumpa De, and G. Majumdar, Parametric optimization of electroless Ni-P-CNT coating using genetic algorithm to maximize the rate of deposition, Materials Today: Proceedings, Jun, 2022. https://doi.org/10.1016/j.matpr.2022.06.106
Sarkar, S., Mukherjee, A., Baranwal, R. K., De, Jhumpa, Biswas, C., & Majumdar, G., Prediction and parametric optimization of surface roughness of electroless Ni-Co-P coating using Box-Behnken design, Journal of the Mechanical Behavior of Materials, 28(1), 153-161, 2019. https://doi.org/10.1515/jmbm-2019-0017
Sarkar, S., Baranwal, R. K., Lamichaney, S., De, Jhumpa, & Majumdar, G., Optimization of electroless Ni-Co-P coating with hardness as response parameter: A computational approach. Jurnal Tribologi, 18, 81-96. e-ISSN: 2289 7232.
De, Jhumpa, Banerjee, T., Sen, R.S., Oraon, B. and Majumdar, G., Multi-objective optimization of electroless ternary Nickel–Cobalt–Phosphorous coating using non-dominant sorting genetic algorithm-II. Engineering science and technology, an international journal, 19(3), pp.1526-1533, 2016. https://doi.org/10.1016/j.jestch.2016.04.011
De, Jhumpa, Biswas, N., Rakshit, P., Sen, R.S., Oraon, B. and Majumdar, G., Computation and optimisation of electroless Ni-Cu-P coating using evolutionary algorithms, ARPN J. Eng. Appl. Sci, 10(5), pp.2273-2283. ISSN 1819-6608, 2015.
De, Jhumpa, Biswas, N., Sen, R. S., & Majumdar, G., Characterization of Ni-Cu-P ternary electroless coating using AFM and SEM, International Journal of Applied Engineering Research., 10(11), 10400-10404, ISSN 0973-4562, 2015.
Biswas, N., De, Jhumpa, Sen, R.S., Oraon, B. & Majumdar, G., Prediction of surface roughness and application of response surface methodology to develop a mathematical model, International journal of innovative research in science, engineering and technology., 2 (7), 2771-2777, ISSN: 2319-8753, 2013.
Sarkar, S., Baranwal, R. K., Nandi, R., Dastidar, M. G., De, Jhumpa, & Majumdar, G., Parametric Optimization of Surface Roughness of Electroless Ni-P Coating. In Optimization Methods in Engineering, Select Proceedings of CPIE 2019 (pp. 197-207), Springer Singapore, 2021. https://doi.org/10.1007/978-981-15-4550-4_12
Jhumpa De, Sagnik Biswas, Pratyusha Rakshit and Rajat Subhra Sen, Parametric Optimization and Prediction of Surface Roughness of Electroless Ni-P Coating Using Differential Evolution Algorithm, Twenty First International Symposium on Processing and Fabrication of Advanced Materials, December 2012. ISBN 978-93-82332-15-2 (Vol. 1), ISBN 978-93-82332-17-6 (Set).
Sarkar, S., De, Jhumpa, Sen, R. S., Oraon, B., & Majumdar, G., Parametric Analysis of Surface Roughness of Electroless Ni-Co-P Coating using Response Surface Method, National Conference on Advanced Materials, Manufacturing and Metrology, CSIR-Central Mechanical Engineering Research Institute, Durgapur, India (pp. 306-312) (ISBN: 978-93-87480-56-8).

Student Projects:

Title: Synthesis, characterization and parametric optimization considering deposited mass per unit area, surface free energy and surface roughness of electrolytic Nickel coating as responses
Students: Akashdip Mahapatra, Arghya Biswas, Suman Maji, Pradipta Ghosh, Arno Baksi, Divyamani Gurung

Supervisors: Dr. Jhumpa De (ME Department) and Dr. Dabamalya Ghosh (Engineering Science and Humanities Department)

Year: 2025