Lenin N

Scopus Publications

Machine learning–assisted evolutionary optimization of hole quality and surface integrity in abrasive waterjet drilling of polycarbonate
J. Bharani Chandar, C. Rathinasuriyan, N. Lenin, M. Sivakumar, Robert Čep, Sachin Salunkhe
Scientific Reports, 2026
Abrasive Waterjet Machining (AWJM) is a reliable non-traditional technology for machining polymer-based engineered materials with low thermal degradation, dimensional inaccuracy, and surface damage. This study examines polycarbonate hole drilling performance using 125 full-factorial experimental trials using process parameters water pressure, standoff distance, and traverse rate. With a coordinate measuring machine and stylus-based profilometer, kerf angle, entry and exit circularity, and surface roughness were measured in a detailed metrological research. Four ML models were created to develop accurate predicting capabilities, with the Random Forest (RF) model performing best in all responses. RF predicted processes accurately with high coefficient-of-determination R 2 values (0.92, 0.82, 0.82, and 0.92) and low error coefficients (RMSE 0.196, 0.045, 0.042, and 0.452). The best drilling settings were found using evolutionary algorithms like Biogeography-Based Optimization (BBO), Particle Swarm Optimization (PSO), Salp Swarm Optimization (SSO), and Tug-of-War Optimization (TWO). Deng’s similarity metric ranked SSO as the best optimizer based on many responses. The SSO produced the optimal settings (W p = 250 MPa, S d = 1.5, and T r = 300 mm/min) for reduced kerf deviation, circularity and surface roughness. Under optimal circumstances, anticipated responses matched experimental results, proving the integrated ML-optimization framework’s strength.
Investigation of hole geometry and surface integrity in abrasive water jet drilling of UHMWPE for orthopedic knee implant applications
K. Subramani, C. Rathinasuriyan, J. Bharani Chandar, N. Lenin, Robert Čep, Sachin Salunkhe
Journal of Materials Research and Technology, 2026
Microstructure Evaluation and Fatigue Properties of Automated DC Pulsed MIG Welding of Monel 400
B. K. Parrthipan, P. Narayanasamy, R. Rajaprasanna, T. Ramkumar, N. Lenin
Journal of Materials Engineering and Performance, 2026
A novel predictive model for abrasive waterjet deep hole drilling on AL7075 T6 using machine learning and evolutionary algorithmic approach
J. Bharani Chandar, M. Sivakumar, N. Lenin, Robert Čep, Sachin Salunkhe, Emad Abouel Nasr, C. Rathinasuriyan
Scientific Reports, 2025
Abrasive Waterjet (AWJ) is a promising non-traditional method for precision cutting of aerospace materials like AL7075 T6. This work explores AL7075 T6 AWJ-based Deep Hole Drilling (DHD) using a full factorial design with accurate modeling and optimization performed through machine learning and evolutionary algorithms. The objective is to investigate the influence of process parameters and to model, and predict the optimal AWJ-DHD settings, such as waterjet pressure, standoff distance, and abrasive mass flow rate, on drilling qualities including geometrical and dimensional precision (kerf angle, kerf ratio), surface roughness, and drilling efficiency. Four machine learning models, Adaptive Boosted Regression (ABR), Extreme Gradient Boosting (XGB), Decision Tree (DT), and Random Forest (RF) were developed with experimental data to enhance prediction accuracy and process efficiency. Among the developed models, RF had the lowest testing error value for all responses with root mean square values of 0.046 (kerf angle), 0.0078 (kerf ratio), 0.044 (surface roughness), and 0.027 (drilling rate). Moth-Flame Optimization (MFO), Differential Evolution (DE), and Sine Cosine Algorithm (SCA) were used for multi-response optimization of AWJ deep hole drilling parameters. The optimal algorithm for each response was selected using Deng's similarity-based ranking. The ranking revealed SCA algorithm outperformed MFO and DE. The SCA algorithm discovered optimal parameter setting for AWJ-DHD as a water pressure of 350 MPa, standoff distance of 1.5 mm, and an abrasive mass flow rate of 300 g/min. Under these conditions, the predicted responses were a kerf angle of 0.048⁰, kerf ratio of 0.011, a surface roughness of 1.438 μm, and a drilling rate 0.769 mm/s. The validation trials using optimized parameters yielded a kerf angle of 0.047⁰, a kerf ratio of 0.066, a surface roughness of 1.40 μm, and a drilling rate of 0.769 mm/s, with percentage variations of 2.08%, 3.03%, 2.14%, and 2.65%, respectively, thereby demonstrating the efficiency of the developed machine learning model and optimization technique. The integrated machine learning and evolutionary algorithm framework improved drilling efficiency and hole quality by minimizing surface roughness.
Process parameter optimization for minimizing overcut in abrasive waterjet deep hole drilling of SS 316L
J. Bharani Chandar, N. Lenin, Alagar Karthick, Pradeep Kumar Singh, M. Siva Kumar, Md Irfanul Haque Siddiqui, Intesaaf Ashraf, Saurav Dixit, Nikolai Ivanovich Vatin, Choon Kit Chan
Scientific Reports, 2025
The need for precise manufacturing in aerospace, medical, and automotive industries requires an investigation of upscale drilling methods that can achieve small-diameter deep holes with exceptional accuracy. Abrasive Waterjet Drilling (AWJD) has developed as a promising technology due to its distinctive blend of precision and adaptability. Despite several advantages, overcutting is the fundamental obstacle restricting the widespread use of AWJD. The novelty of this research is to investigate the impact of process parameters, specifically water pressure, standoff distance, and abrasive mass flow rate, on the top, bottom, and depth-averaged radial overcut developed during the deep hole drilling of stainless steel 316L material. The deep hole drilling experiments have been conducted utilizing Taguchi’s (L 16 ) orthogonal array by adjusting the drilling settings. The statistical significance of specific drilling parameters and second-order quadratic models for the responses have been established by analysis of variance. Additionally, to mitigate the impact of overcut and improve the drilling quality necessary for diverse sectors such as automotive, biomedical, and oil and gas, a metaheuristic optimization method, specifically the Grasshopper Optimization Algorithm (GHO), has been utilized. Thereafter, the effectiveness of the suggested algorithm has been validated using quality measures, namely hyper-volume and spacing by comparing it to the approaches of whale optimization, harmony search, and multiverse optimization algorithms. The comparison shows that the GHO algorithm outperformed the others. The GHO algorithm identified the optimal process parameters for AWJD as water pressure 305.36 MPa, standoff distance 1.00 mm, and mass flow rate 600 g/min. The anticipated values for the top, bottom, and depth-averaged radial overcut, according to the optimal parameters, are 1.19 mm, 0.64 mm, and 1.53 mm, respectively. Furthermore, a validation test has been conducted to verify the efficacy of the GHO algorithm. The validation test showed top, bottom, and depth-averaged radial overcut values of 1.17 mm, 0.66 mm, and 1.49 mm, with percentage deviations of 1.71%, 3.03%, and 2.68%, respectively, with the GHO algorithm. The surface quality of the drilled holes has been examined through a Scanning Electron Microscope (SEM). The SEM images have been obtained at magnifications of 12X and 500X of the drilled hole surface using optimum parameters, demonstrating smooth and uniform surfaces at the top, middle, and bottom of the drilled hole.
UNRAVELING THE MACHINABILITY OF NIMONIC 263: AN EXPERIMENTAL STUDY USING ABRASIVE WATERJET CUTTING
DEEPAK CHANDRAN GANESH, LENIN NAGARAJAN, BALAJI VASUDEVAN
Surface Review and Letters, 2025
The demand for high-performance materials in aerospace, automotive, and power generation industries has led to the development and utilization of advanced superalloys. Nimonic C-263/263, a nickel-based superalloy, is widely employed in critical components of jet engines, such as combustion chambers, heat exchangers, reformer tubes, gas turbine power generation components like turbine discs, shafts, and blades, due to its exceptional high-temperature strength, corrosion resistance, and creep properties. However, the inherent hardness and toughness of this alloy pose significant challenges for conventional machining processes. Abrasive waterjet cutting (AWJC) emerges as a promising alternative for the efficient machining of such difficult-to-cut superalloy materials. This paper presents an in-depth investigation on the machinability of thick Nimonic 263 superalloy plate material using AWJC, aiming to provide insights into the effects of process parameters, cutting performances, and resulting surface integrity due to change in declination angle measurement. An examination was also conducted on the pattern of striation formation under varying process parameters, utilizing digital microscopic images. The goal of the study is to establish a link between the parameters of striation production and the angle of jet impingement. The results indicate that a decrease in striation during the water jet processing of Nimonic 263 superalloy is caused by an increase in the declination angle.
Performance assessment of different cooling conditions in the sustainable machining of Hastelloy X alloy
Xiaoming Huang, Qian Zhou, Sivakumar Mahalingam, Lenin Nagarajan, Poongavanam Ganesh Kumar, Vinothkumar Sivalingam
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2025
The primary goal of this study is to explore techniques that can enhance the machinability of Hastelloy X under different cooling conditions. L27 orthogonal turning experiments were performed with process parameters such as cutting speed ( vc), feed rate ( f) and depth of cut ( ap) to study the effect of tool wear and machining cost. The control parameters of a procedure were evaluated through a multiple linear regression model (MLRM) derived from the correlation between the output responses and the input parameters. A multi-verse optimisation algorithm (MVO) was proposed for optimisation studies. It achieves this by minimising the tool wear and machining cost. The proposed algorithm was evaluated against the techniques used for particle swarm and salp swarm optimisations. According to the study, the MVO algorithm outperformed other methods to identify the optimal control parameters for a given process. Based on the analysis, the anticipated tool wear duration is 17.57 min, and the machining cost is estimated at 0.934 $/cm3 under cryogenic conditions. Cryogenic cooling reduces chipping and adhesion in cutting by minimising workpiece softening and improving cutting tool hardness.
Exploring materials, technologies, applications, and future outlooks in 4D printing: a comprehensive survey
C. Rathinasuriyan, J. Bharani Chandar, N. Lenin, M. Puviyarasan
Progress in Additive Manufacturing, 2025
Investigating abrasive water jet drilling of YSZ-Coated inconel 718: A surface topography and morphology study
Balaji Vasudevan, Lenin Nagarajan, Md Irfanul Haque Siddiqui, Murugu Nachippan N, Sarangapani Palani, Yuvaraj Natarajan
Materials Chemistry and Physics, 2025
Optimization of cutting force during turning of custom 450 steel using TiAlSiN coated WC tool inserts
Karthick Muniyappan, Ramakrishna Guttula, Siva Kumar M, Nishikant Kishor Dhapekar, Lenin N, Senthilkumar J., Ramanpreet Singh, Vimal Kumar Pathak
International Journal on Interactive Design and Manufacturing, 2025
Study of Abrasive Wear Behavior of Epoxy-Carbon Fiber Composites with Nano Al2O3 Filler: An Algorithmic Approach
B. R. Lokesh Yadhav, H. K. Govindaraju, M. D. Kiran, Narasimha Marakala, Siva Kumar Mahalingam, Lenin Nagarajan, Salah J. Mohammed, Hasan Sh. Majdi, Sameer Algburi, Mohammad Amir Khan
Journal of Bio and Tribo Corrosion, 2025
Enhancing machining efficiency of UNS S45000 alloy steel using cryogenically treated TiAlSiN coated tungsten carbide inserts
S. Baskar, Raman A, Karthick M, Lenin N, Rajesh Kumar, B. Rohini, M. Chandrasekaran, Uma Devi A, Meenambiga Setti Sudharsan, M. Ruban
Results in Engineering, 2025
ENHANCING TOOL LIFE IN END MILLING OF MARTENSITIC STAINLESS-STEEL CUSTOM 450 THROUGH DEEP CRYOGENIC TREATMENT: WEAR ANALYSIS AND PERFORMANCE EVALUATION
SIVA KUMAR MAHALINGAM, C. DEVI, LENIN NAGARAJAN, RAJESH DURVASULU
Surface Review and Letters, 2025
Next-Gen Composites: Impact of Nanopolypropylene on Carbon-Basalt Fiber Reinforced Hybrids
V. Chandran, N. Lenin, Lenka Čepová, D. Rajesh, Jan Blata, M. Siva Kumar, Muniyandy Elangovan
Journal of Natural Fibers, 2025
Exploring surface integrity: Experimental investigation into abrasive waterjet deep hole drilling on ss 316L for biomedical applications
J. Bharani Chandar, N. Lenin, C. Rathinasuriyan
Journal of Alloys and Metallurgical Systems, 2024
Vegetable oils in minimum quantity lubrication: a comparative analysis of properties and performance
Balaji Vasudevan, Lenin Nagarajan, N. Murugu Nachippan, Sivasubramanian Mahadevan
Interactions, 2024
Submerged abrasive water jet machining fixture with adjustable workpiece orientation for enhanced machining flexibility
Balaji Vasudevan, Lenin Nagarajan, N. Murugu Nachippan, Sivasubramanian Mahadevan
Interactions, 2024
Evaluation of engine characteristics of diesel engines fuelled with MWCNT nano additive doped hemp biodiesel blends
N. Murugu Nachippan, S. Padmanabhan, Balaji Vasudevan, Lenin Nagarajan, P. Balamurugan, P. Gopi
Interactions, 2024
Optimization of dual pulse resistance welding parameters for ASTM A240 stainless steel sheets: a multi-objective approach
D. Rajesh, V. Chandran, N. Lenin, A. Subramanian, M. Deva, P. Balamurugan
Interactions, 2024
Unlocking the potential of copper-reinforced fibre metal laminates: The influence of stacking sequences
Balaji Vasudevan, Lenin Nagarajan, Murugu Nachippan N, Rajesh Durvasulu, Mushtaq Ahmad Ansari
Journal of Materials Research and Technology, 2024
Correction to: A Novel Equal Area-Equal Width-Equal Bin Numbers Technique Using Salp Swarm Optimization Algorithm for Maximizing the Success Rate of Ball Bearing Assembly (International Journal of Precision Engineering and Manufacturing, (2024), 25, 10, (2039-2058), 10.1007/s12541-024-01048-x)
Lenin Nagarajan, Siva Kumar Mahalingam, Robert Cep, Janjhyam Venkata Naga Ramesh, Muniyandy Elangovan, Faruq Mohammad
International Journal of Precision Engineering and Manufacturing, 2024
A Novel Equal Area-Equal Width-Equal Bin Numbers Technique Using Salp Swarm Optimization Algorithm for Maximizing the Success Rate of Ball Bearing Assembly
Lenin Nagarajan, Siva Kumar Mahalingam, Robert Cep, Janjhyam Venkata Naga Ramesh, Muniyandy Elangovan, Faruq Mohammad
International Journal of Precision Engineering and Manufacturing, 2024
Machinability study on abrasive waterjet drilling of YSZ-coated inconel 718 superalloy: optimization and modelling by machine learning
Mahalingam Siva Kumar, Lenin Nagarajan, Balaji Vasudevan
Engineering Research Express, 2024
Experimental study, modeling, and parametric optimization on abrasive waterjet drilling of YSZ-coated Inconel 718 superalloy
Balaji Vasudevan, Lenin Nagarajan, Natrayan L, Alagar Karthick, Siva Kumar Mahalingam, Chander Prakash, Choon Kit Chan, Hitesh Panchal, Md Irfanul Haque Siddiqui
Journal of Materials Research and Technology, 2024
Analyzing the impact of process variables on erosion diameter and taper angle in abrasive water-jet drilling of YSZ-coated Inconel 718 superalloy
Lenin Nagarajan, Balaji Vasudevan, Siva Kumar Mahalingam
Advanced Composites, 2024
Abrasive waterjet drilling process enhancement using machine learning and evolutionary algorithms
Lenin Nagarajan, Siva Kumar Mahalingam, Balaji Vasudevan
Materials and Manufacturing Processes, 2024
COMPREHENSIVE MACHINABILITY STUDY ON AWJM OF NIMONIC-263 SUPERALLOY AND PARAMETRIC OPTIMIZATION
DEEPAK CHANDRAN GANESH, LENIN NAGARAJAN, SIVA KUMAR MAHALINGAM, BALAJI VASUDEVAN
Surface Review and Letters, 2024
Enhancing surface finish in machining Nimonic 263 superalloy using submerged abrasive water-jet cutting
Deepak Chandran Ganesh, Lenin Nagarajan, Siva Kumar Mahalingam, Balaji Vasudevan
Advanced Composites, 2024
THE TLBO ALGORITHM-BASED OPTIMAL HEAT TRANSFER PARAMETERS PREDICTION OF Al2O3 WATER NANOFLUIDS IN VARIABLE PITCH CORRUGATED TUBE HEAT EXCHANGER
Vikas Ugle, Lenin Nagarajan, Arulprakasajothi Mahalingam, Rajan Thekkineydeth
Thermal Science, 2024
Experimental analysis and optimization of abrasive waterjet deep hole drilling process parameters for SS AISI 316L
Bharani Chandar J, N. Lenin, Siva Kumar, Naveen Kumar Gupta, Alagar Karthick, Rathina Suriyan, Hitesh Panchal, Abhinav Kumar, Anand Patel, Kishor Kumar Sadasivuni
Journal of Materials Research and Technology, 2023
Minimization of total manufacturing cost of a knuckle joint assembly by means of optimal tolerance allocation using various optimization algorithms
Vignesh Kumar Dharmaraj, Ravindran Durairaj, Nagarajan Lenin, Mahalingam Siva Kumar, Arul Marcel Moshi Antony Joseph Decruz
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 2023
Effect Of Stand-Off Distance On Abrasive Water Jet Piercing Of Ceramic-Coated Superalloy
V. BALAJI, N. YUVARAJ, N. LENIN, A. MANJUNATH
Surface Review and Letters, 2023
Parameter optimization of titanium-coated stainless steel inserts for turning operation
Karthick Muniyappan, Lenin Nagarajan
International Journal for Simulation and Multidisciplinary Design Optimization, 2023
Application of Conceive, Design, Implement and Operate (CDIO) Strategy for the Development of Engineering Education in Indian Perspective
N. Lenin, M. Siva Kumar, G. Selvakumar
Journal of Education, 2023
Experimentation and optimization of deep hole drilling parameters for SS316L
Bharani Chandar Jayaprakash, N. Lenin, M. Siva Kumar
Materials and Manufacturing Processes, 2023
INVESTIGATION ON SILVER-WATER NANOFLUID FOR DEVELOPMENT OF NEW VISCOSITY CORRELATION
S. Iyahraja, J. Selwin Rajadurai, M. Sivakumar, N. N. Lenin
Bulletin of the Chemical Society of Ethiopia, 2023
Enhancement of Thermal Behaviour of Flax with a Ramie Fibre-Reinforced Polymer Composite
Durvasulu Rajesh, Nagarajan Lenin, Robert Cep, Palanivel Anand, Muniyandy Elangovan
Polymers, 2023
Experimental Investigation of Bi-Directional Flax with Ramie Fibre-Reinforced Phenol-Formaldehyde Hybrid Composites
Durvasulu Rajesh, Nagarajan Lenin, Robert Cep, Palanivel Anand, Muniyandy Elangovan
Polymers, 2022
An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method
Siva Kumar Mahalingam, Lenin Nagarajan, Chandran Velu, Vignesh Kumar Dharmaraj, Sachin Salunkhe, Hussein Mohamed Abdelmoneam Hussein
Applied Sciences Switzerland, 2022
A novel approach in selective assembly with an arbitrary distribution to minimize clearance variation using evolutionary algorithms: a comparative study
Lenin Nagarajan, Siva Kumar Mahalingam, Jayakrishna Kandasamy, Selvakumar Gurusamy
Journal of Intelligent Manufacturing, 2022
Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge
Vengatajalapathi Nagarajan, Ayyappan Solaiyappan, Siva Kumar Mahalingam, Lenin Nagarajan, Sachin Salunkhe, Emad Abouel Nasr, Ragavanantham Shanmugam, Hussein Mohammed Abdel Moneam Hussein
Applied Sciences Switzerland, 2022
Dynamic Analysis and Fabrication of Single Screw Conveyor Machine
Sundarraj Moorthi, Meikandan Megaraj, Lenin Nagarajan, Alagar Karthick, Murugesan Bharani, Pravin P. Patil
Advances in Materials Science and Engineering, 2022
Harmony search algorithm for simultaneous minimization of bi-objectives in multi-row parallel machine layout problem
N. Lenin, M. Siva Kumar
Evolutionary Intelligence, 2021
RECENT RESEARCH PROGRESS in DEEP HOLE DRILLING PROCESS: A REVIEW
J. BHARANI CHANDAR, L. NAGARAJAN, M. SIVA KUMAR
Surface Review and Letters, 2021
A novel methodology for simultaneous minimization of manufacturing objectives in tolerance allocation of complex assembly
Lenin Nagarajan, Siva Kumar Mahalingam, Sachin Salunkhe, Emad Abouel Nasr, Jõao Paulo Davim, Hussein M. A. Hussein
Applied Sciences Switzerland, 2021
Harmony search algorithm for minimizing assembly variation in non-linear assembly
Siva Kumar Mahalingam, Lenin Nagarajan, Sachin Salunkhe, Emad Abouel Nasr, Jõao Paulo Davim, Hussein M. A. Hussein
Applied Sciences Switzerland, 2021
Optimization of process parameters for turning hastelloy x under different machining environments using evolutionary algorithms: A comparative study
Vinothkumar Sivalingam, Jie Sun, Siva Kumar Mahalingam, Lenin Nagarajan, Yuvaraj Natarajan, Sachin Salunkhe, Emad Abouel Nasr, J. Paulo Davim, Hussein Mohammed Abdel Moneam Hussein
Applied Sciences Switzerland, 2021
Optimization of process control parameters for wedm of AL-LM25/fly ASH/B4C hybrid composites using evolutionary algorithms: A comparative study
Nagarajan Lenin, Mahalingam Sivakumar, Gurusamy Selvakumar, Devaraj Rajamani, Vinothkumar Sivalingam, Munish Kumar Gupta, Tadeusz Mikolajczyk, Danil Yurievich Pimenov
Metals, 2021
Strategy to reduce floor area and flow distance of products in multi-row parallel machine layout design using cuckoo search algorithm
Siva Kumar Mahalingam, Lenin Nagarajan
Journal of Advanced Manufacturing Systems, 2021
Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm
Mahalingam Sivakumar, Nagarajan Lenin, Kandasamy Jayakrishna, Natarajan Eswara Prasath
Mathematical Problems in Engineering, 2021
Supply Chain Adoption During COVID-19 Lockdown: Global Scenario
Lenin Nagarajan, Siva Kumar Mahalingam, Jayakrishna Kandasamy, Selvakumar Gurusamy
Management and Industrial Engineering, 2021
Thermal and electrical performance evaluation of PV/T collectors in Southern India
N. Lenin, P. Sivamurugan, B. Srimanickam, P. Ramanan, M. Siva Kumar
International Journal of Ambient Energy, 2021
Solution for multi-objective single row facility layout problem using PSO algorithm
Lenin Nagarajan, Siva Kumar Mahalingam, Gurusamy Selvakumar, Jayakrishna Kandasamy
Data Driven Optimization of Manufacturing Processes, 2020
Free Vibration and Buckling Analysis of FG-CNT Plates
Lenin Nagarajan, I. Mohammed Irfan
Lecture Notes in Mechanical Engineering, 2020
Preface
Lecture Notes in Mechanical Engineering, 2020
Application of artificial immune system algorithm for simultaneous minimisation of multiple objectives in single row facility layout problems
Lenin Nagarajan, Siva Kumar Mahalingam, Jayakrishna Kandasamy, Selvakumar Gurusamy
International Journal of Industrial and Systems Engineering, 2020
Intelligent Fixture Layout Design for End Milling Process Using Artificial Neural Networks
F. Michael Thomas Rex, D. Ravindran, A. Andrews, Lenin Nagarajan
Lecture Notes in Mechanical Engineering, 2020
Enhancement of mechanical characterization of aluminum alloy with tungsten carbide metal matrix composite by particulate reinforcements
P. Anand, D. Rajesh, N. Lenin, V. Balaji, V.K. Bupesh Raja, K. Palanikumar
Materials Today Proceedings, 2020
Experimental investigation on the mechanical properties of carbon-glass-jute fiber reinforced epoxy hybrid composites
S. Velu, J.K. Joseph, M. Sivakumar, V.K. Bupesh Raja, K. Palanikumar, N. Lenin
Materials Today Proceedings, 2020
Investigations on the mechanical properties of tungsten carbide reinforced aluminium metal matrix composites by stir casting
D. Rajesh, P. Anand, N. Lenin, V.K. Bupesh Raja, K. Palanikumar, V. Balaji
Materials Today Proceedings, 2020
Brake squeal analysis of disc brake
V. Balaji, N. Lenin, P. Anand, D. Rajesh, V.K. Bupesh Raja, K. Palanikumar
Materials Today Proceedings, 2020
Quantitative and qualitative analysis for isochoric heating method during pressure development of phase change process
P. Sivamurugan, Irudayaraj Sebastin, N. Lenin
International Journal of Ambient Energy, 2019
Development, mechanical characterization and analysis of dry sliding wear behavior of AA6082-Metakaolin metal matrix composites
Renjin J Bright, G Selvakumar, M Sumathi, N Lenin
Materials Research Express, 2019
Effects of multi-pass cutting during wire electrical discharging
Gurusamy Selvakumar, Velayutham Balasubramanian, Sundaravel Vijayan, Nagarajan Lenin
Materialpruefung Materials Testing, 2019
Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm
D Vignesh Kumar, D Ravindran, N Lenin, M Siva Kumar
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2019
Experimental studies on different proportions of cb-filled natural rubber composites with precipitated silica and silica gel
V. Chandran, N. Lenin, T. Maridurai, P. Jawahar, G. Vignesh, K. Sunil
Lecture Notes in Mechanical Engineering, 2019
Evaluation of vibration damping behavior of different sizes of waste tyre rubber in natural rubber composites
V Chandran, Lenin Nagarajan, Manvel Raj Thomas
Journal of Composite Materials, 2018
Experimental study on abrasive water jet machining of AA5083 in a range of thicknesses
Gurusamy Selvakumar, Shanmuga Sundaram Ram Prakash, Nagarajan Lenin
International Journal of Abrasive Technology, 2018
Solution for bi-objective single row facility layout problem using artificial bee colony algorithm
Lenin Nagarajan, Siva Kumar Mahalingam, Selvakumar Gurusamy, Vignesh Kumar Dharmaraj
European Journal of Industrial Engineering, 2018
Optimization of process parameters of ECM by RSM on AISI 202 steel
Carbon Science and Technology, 2015
A tabu search for multi-objective single row facility layout problem
N. Lenin, M. Siva Kumar, D. Ravindran, M. N. Islam
Journal of Advanced Manufacturing Systems, 2014
Multi-objective optimization in single-row layout design using a genetic algorithm
N. Lenin, M. Siva Kumar, M. N. Islam, D. Ravindran
International Journal of Advanced Manufacturing Technology, 2013
A simple heuristic for linear sequencing of machines in layout design
M. Siva Kumar, M.N. Islam, N. Lenin, D. Vignesh Kumar, D. Ravindran
International Journal of Production Research, 2011

Lenin N

RESEARCH INTERESTS

Scopus Publications