Engineering, Mechanical Engineering, Industrial and Manufacturing Engineering, Metals and Alloys
55
Scopus Publications
988
Scholar Citations
16
Scholar h-index
33
Scholar i10-index
Scopus Publications
Optimization of CNC turning of Al 1100 grade alloy using response surface methodology (RSM) and machine learning algorithms Mahesh Gopal, Lemi Negera Woyessa, Jabesa Adula, Jaleta Sori Nagasa, Edosa Ketema Kelbesa, Adugna Fikadu Geleta Engineering Solid Mechanics, 2026 Aluminum 1100 is a commercially pure aluminum alloy with properties suitable for applications requiring ductility and workability. It is soft, weldable, and corrosion-resistant. This study attempts to determine the influence of machining on high-speed turning operations. The experiment is designed using the Design of Experiments of Response Surface Methodology, using input parameters such as cutting speed, feed rate, and cutting depth, to estimate surface roughness, temperature, and machining time of aluminum1100 as the workpiece material, with a carbide tool used for operation. The Analysis of Variance technique has been used to test the material's performance. In contrast, the Design Expert software has been used to study the impact of cutting parameters on the workpiece. A Backpropagation ANN model is developed in MATLAB to optimize cutting parameters and reduce Ra, T, and Tm values. The ANN indicates that the lowest expected value is in this case. The Multi-Objective Genetic Algorithms are employed to forecast turning parameters, and it is observed that, for an input parameter grouping of 16 Pareto-optimal solution sets, the ideal Ra ranges from 1.37 to 1.62 µm, and the temperature ranges from 34.10 to 34.08 °C. The machining time ranges from 1.27 to 1.34 min. Among all, cutting speed has the greatest influence on the parameter. The confirmatory analysis shows that the experimental and predicted values differ by less than ±2% and agree admirably with the experimental values.
Unveiling the armor: experimental study on ballistic impact resistance of silica aerogel, sorbothane, and graphite sandwich composites S. L. Pradeep Kumar, B. Deeban, D. Santhosh Kumar, P. Baranitharan, Malinee Sriariyanun, Mahesh Gopal Discover Materials, 2025 Ballistic armour is intended to save lives by absorbing the impact of bullets and preventing them from entering the wearer’s body. Especially for police enforcement, security, and military professionals. However, the research on armor that withstanding the impacts of shockwaves during the explosives are limited. This study evaluates a singular sandwich composite of silica aerogel, Sorbothane, and graphite (SiSG) against explosive shockwaves and ballistic impacts, marking the inaugural dual-mode assessment of lightweight structural armour. This optimised structure surpasses traditional materials in explosive and ballistic situations due to the unique amalgamation of aerogel’s exceptionally low density and thermal resistance, Sorbothane’s exceptional damping characteristics, and graphite’s elevated stiffness. To more accurately replicate real-world settings, we may get insights on item reactions upon impact near the target by conducting ballistic experiments at a confined range of 2 m, far less than the standard 55 m. The optimal, previously unreleased layer sequence—graphite strike face, aerogel intermediate, and Sorbothane backing—was established by combining these trials with extensive ANSYS finite-element simulations. The absence of apparent surface damage in shockwave-treated SiSG panels sets a new standard for the blast resistance of lightweight composites. This extensive set of testing and computer simulations establishes the foundation for next-generation multifunctional armour systems are the novelties which elicits this research. The experimental findings reveal that the sandwich composite, comprising silica aerogel, Sorbothane, and graphite layers, demonstrates superior resilience against both shockwaves and bullet impacts. The shockwave-treated materials exhibit no physical damage on their outer surfaces, affirming their ability to withstand explosions. This research underscores the critical role of material selection and design in enhancing structural resilience, contributing significantly to the advancement of material science and engineering.
Tribological Study of Multi-Walled Carbon Nanotube-Reinforced Aluminum 7075 Using Response Surface Methodology and Multi-Objective Genetic Algorithm Endalkachew Mosisa Gutema, Mahesh Gopal, Hirpa G. Lemu Journal of Composites Science, 2025 Aluminum metal matrix composites (AlMMCs) are widely employed in the aerospace and automotive industries due to their greater qualities in comparison to the base alloy. Adding nanocomposites like multi-walled carbon nanocomposites (MWCNTs) to aluminum enhances its mechanical properties. In the current research, aluminum 7075 with MWCNT particles was prepared and characterized to study its tribological behaviors, such as its hardness and specific wear rate. The experiment was designed with varying weight percentages of MWCNTs of 0.5, 1.0, and 1.5, and these were fabricated using powder metallurgy, employing compacting pressures of 300, 400, and 500 MPa and sintering temperatures of 400, 450, and 500 °C. Further, the experimental setup was designed using Design-Expert V13 to examine the impact of influencing parameters. A second-order mathematical model was developed via central composite design (CCD) using a response surface methodology (RSM), and the performance characteristics were analyzed using an analysis of variance (ANOVA). The hardness (HV) and specific wear rate (SWR) were measured using a hardness tester and pin-on-disk apparatus. From the results thus obtained, it was observed that an increase in compacting pressure and sintering temperature tends to increase the hardness and specific wear rate. An increasing weight percentage of MWCNTs increased their hardness, while the SWR was less between the weight percentages 0.9 and 1.3. A multi-objective genetic algorithm (MOGA) was trained and evaluated to provide the best feasible solutions. The MOGA suggested sixteen sets of non-dominated Pareto optimal solutions that had the best and lowest predicted values. The confirmatory analytical results and predicted characteristics were found to be excellent and consistent with the experiential values.
A Structural Equation Model For Adopting Additive Manufacturing in the Footwear Firms Supply Chains Tekalign Lemma, Endalkachew Mosisa Gutema, Hirpa G. Lemu, Mahesh Gopal Brazilian Journal of Operations and Production Management, 2025 Goal: The objective of this research is to present a theoretical framework and explore how additive manufacturing (AM) techniques affect supply chain complexity (SCC) in the footwear sector. Design/Methodology/Approach: This study developed theoretical framework that includes AM best practices and SCC through extensive literature review. Using 1-5 likert scale surveys, data were gathered from 205 professionals working in 29 Ethiopian footwear industries in the period October 20 to December 23, 2023. The collected questionnaires were tested for reliability and validity, measurement and structural model fit test were checked using confirmatory factor analysis. Structural Equation Modeling using AMOS v23 was used to evaluate the proposed correlations. Results: The confirmatory factor analysis test result revealeld that measurement and structural equation model fit test fulfill the model fit test requirements, i.e. χ2/df < 5, CFI, GFI and TLI > 0.9, RMR and RMSEA < 0.08. The findigs of the study confirmed that additive manufacturing best practices (time, inventory, operation, and resource, energy and waste related factors) have positive effects on static and dynamic supply chain complexity. Practical implications: This study helps the firm to focus on adoptation of AM for improving supply chain complexity. Furthermore, this study extended earlier research in the domains of SCM by building a theoretical framework that connects AM best practices with supply chain complexity factors. Originality/value: This work bridges the scientific knowledge gap by combining supply chain complexity and AM best practices. Among others, it can contribute to the existing literature by illustrating the benefits of adopting AM technology particularly in footwear sector.
Numerical Simulation of Temperature Distribution and Residual Stress in Laser Beam Welding AA6061 and Ti-6Al-4V and Optimization of Welding Processes Alemu Merga, Endalkachew Mosisa Gutema, Mahesh Gopal, Hirpa G. Lemu Recent Patents on Engineering, 2025 Background: Since the combination of its rapid processing speed and high energy input, laser beam welding is considered advanced and suitable for welding thin and lightweight metals. The residual stresses deposited in the parts as a result of rapid heating and cooling render laserwelded components susceptible to fractures and deformities. Objective: In this patent, the modelling of the laser beam welding process during the joining of Ti- 6Al-4V and AA6061 dissimilar metals to analyze the effects of the welding process on residual stress and elastic strain by considering beam radius, beam offset, welding speed, and beam power as input parameters. Methods: The 3D model of the Ti-6Al-4V and AA6061 was developed using CATIA V5R16 software and beam radius. Beam offset, welding speed, and beam power are the input parameters considered, and the output parameters are stress and elastic strain. Design Expert is used to design the experiment. ANOVA was used, and a mathematical model was developed to analyze the performance characteristics of the welding process. Results: The results revealed that increasing the laser power increases residual stress, whereas it decreases with increasing the other parameters. The maximum average equivalent von Mises stress was 288.79 MPa, which is near the yield strength of AA6061. The optimum welding conditions selected for minimum possible residual stress is 1600.003 W, welding speed 0.05 m/s, beam radius 0.014 m. Conclusion: Based on the current observation during the simulation of joining dissimilar metals, the flow temperature along the weld line and weldment shows uneven distribution due to the dissimilarity of temperature-dependent properties of materials. The increased laser power leads to an increase in residual stress.
Comparative Study of Machining Parameters of Single and Double Cutting Tools During Turning of AISI 1045 Steel Atomsa Demiso Hirpa, Endalkachew Mosisa Gutema, Hirpa G. Lemu, Mahesh Gopal Recent Patents on Engineering, 2025 Background: The patent of cutting operations is carried out with a cutting tool that is fed parallel to or at right angles to the work axis. The main objective of this study is to minimize surface roughness and MRR. Objective: The effect of cutting parameters on surface roughness and material removal rate is investigated using AISI 1045 steel as a workpiece material, and single and double carbide cutting tools are used under dry machining conditions. Methods: The cutting speed, feed rate, and depth of cut are considered input parameters for experimental purposes. Taguchi L9 orthogonal array design of experiments is used for designing the experiments. Parameters are optimized using Taguchi L9 orthogonal design of experiments and Analysis of Variance (ANOVA). MINITAB 17 software is used to solve the coefficients of the regression model. Results: The result indicates that the cutting speed was the most significant influencing factor that affects the surface roughness, followed by feed rate and depth of cut for both single and doublecutting tools. Conclusion: The minimum surface finish for the best cutting parameter was 0.95 μm for a single and 0.92 μm for a double-turning tool. The highest material removal rates for single and double turning were 6456 mm3/min and 6603 mm3/min. The result shows that while using double tools, the rate of material removal rate increased and the machining time decreased.
Introduction to Energy Storage Systems P. Suresh Kumar, J. Niresh, G. Mahesh, S. Settu, G. Tamilselvan, Robin Singh, Neha Tiwari, Ramesh K. Guduru Electrolytes for Energy Storage Applications Fundamentals and Advances, 2024
Optimization of machining parameters on temperature rise in CNC turning process of aluminium 6061 using rsm and genetic algorithm International Journal of Modern Manufacturing Technologies, 2020
Application of heuristic techniques and effect of process parameter on turning and facing operation-a review (2010-2015) Arpn Journal of Engineering and Applied Sciences, 2016
Prediction and optimization of process parameters on A22e (Bimetal Bearing) using RSM and genetic algorithm International Journal of Applied Engineering Research, 2015
Prediction and optinization of tool wear on A22E (bimetal bearing material) using rsm and genetic algorithm International Journal of Mechanical and Mechatronics Engineering, 2015
Experimentation and prediction of surface roughness of the machining parameter with reference to the rake angle in end mill International Review of Mechanical Engineering, 2012
Experimentation and prediction of vibration amplitude in end milling with reference to radial rake angle International Review of Mechanical Engineering, 2012
RECENT SCHOLAR PUBLICATIONS
You are entitled to access the full text of this document Optimization of CNC turning of Al 1100 grade alloy using response surface methodology (RSM) and machine learning … EKKAFG Mahesh Gopal, Lemi Negera Woyessa, Jabesa Adula, Jaleta Sori Nagasa Engineering Solid Mechanics , 2026 2026
A comparison of hardness and wear behaviour of two 3D printed materials and its optimization N Rajesh, GG Mahesh, R Lokanadham AIP Conference Proceedings 3385 (1), 040012 , 2026 2026
Learning-based Approach for Early Detection of Hardware Trojans in Open-Source Hardware P Bhargavi, NS Samhita, G Mahesh, H Narayana, KA Kumar 2025 4th International Conference on Automation, Computing and Renewable … , 2025 2025
Challenges and Solutions of Industry 4.0 to Industry 5.0 N Rajesh, KJ Narayana, GG Mahesh, R Lokanadham Manufacturing in the Digital Age, 117-129 , 2025 2025
Unveiling the armor: experimental study on ballistic impact resistance of silica aerogel, sorbothane, and graphite sandwich composites SLP Kumar, B Deeban, DS Kumar, P Baranitharan, M Sriariyanun, ... Discover Materials 5 (1), 224 , 2025 2025 Citations: 3
Enhanced Strength and Microstructure of AA7075 Matrix Composites Reinforced with SiC and TiC Particles GSP Rao, GG Mahesh, N Mohammed, MD Babu, P Kumar, M Bhanja Annales de Chimie. Science des Materiaux 49 (5), 497 , 2025 2025
Numerical Simulation of Temperature Distribution and Residual Stress in Laser Beam Welding AA6061 and Ti-6Al-4V and Optimization of Welding Processes A Merga, EM Gutema, M Gopal, HG Lemu Recent Patents on Engineering 19 (4), E260624231323 , 2025 2025 Citations: 2
Comparative Study of Machining Parameters of Single and Double Cutting Tools During Turning of AISI 1045 Steel AD Hirpa, EM Gutema, HG Lemu, M Gopal Recent Patents on Engineering 19 (4), E020724231489 , 2025 2025 Citations: 1
Tribological Study of Multi-Walled Carbon Nanotube-Reinforced Aluminum 7075 Using Response Surface Methodology and Multi-Objective Genetic Algorithm EM Gutema, M Gopal, HG Lemu Journal of Composites Science 9 (3), 137 , 2025 2025 Citations: 3
A comparative study of network slicing techniques for effective utilization of channel for 5g and beyond 5g networks PB Metre, G Kalnoor, G Mahesh, S Gowrishankar IEEE Access , 2025 2025 Citations: 13
A Structural Equation Model For Adopting Additive Manufacturing in the Footwear Firms Supply Chains T Lemma, EM Gutema, HG Lemu, M Gopal Brazilian Journal of Operations & Production Management 22 (1), 2322-2322 , 2025 2025 Citations: 2
Exploring the Mechanical Behaviour of Primary Suspension Springs in Locomotives GG Mahesh, PD Prasad, AV Gopal, KY Kumar International Journal of Vehicle Structures & Systems 17 (6), 955-957 , 2025 2025
Pretreated wheat straw blended with coffee husk biomass resources for the generation of biogas energy Y Solomon, M Gopal International Journal of Ambient Energy 45 (1), 2384943 , 2024 2024 Citations: 1
Revolutionizing agriculture: a comprehensive research on IoT controlled fluid circulation in hydroponics using plumbed PVC planting pipes for enhanced crop growth and disease … A Bovas Herbert Bejaxhin, Y Brucely, DD Rose, G Mahesh, S Sharma, ... Environmental Fluid Mechanics 24 (6), 1235-1262 , 2024 2024 Citations: 7
Multi-Response Optimization and the Effect of Parameters in Turning of AISI 4140 Steel using (Al2O3+ CuO) Hybrid Nanofluid under MQL Approach A Merga, EM Gutema, M GOPAL 2024 Citations: 1
NETWORK SLICING FOR 5G AND BEYOND 5G: A COMPREHENSIVE SURVEY PB Metre, G Mahesh, S Gowrishankar Journal of Data Acquisition and Processing 39 (1), 1248-1258 , 2024 2024
NETWORK SLICING: ENABLING EFFICIENT RESOURCE ALLOCATION AND SERVICE CUSTOMIZATION IN FUTURE NETWORKS PB Metre, G Mahesh, S Gowrishankar Journal of Data Acquisition and Processing 39 (1), 1218-1226 , 2024 2024
Machine Learning Based Surface Finish Prediction and Optimization of Process Parameters in Pulsed CO 2 Laser Cutting of Particle (TiC) Reinforced Al6061 … M Arunadevi, S Saravanan, G Mahesh, S Chethan Journal of The Institution of Engineers (India): Series D, 1-10 , 2024 2024 Citations: 5
Feature Engineering and Hybrid Machine Learning Approach for Flight Delay Prediction J Ningthoukhongjam, G Mahesh, MS Alam, P Kumar, K Kumar 2024 International Conference on Data Science and Network Security (ICDSNS), 1-6 , 2024 2024 Citations: 5
Reduction of cutting temperature effect and surface deficiencies on CNC turned AZ91 Mg alloy with fluidized nano oxide coolants G Mahesh, D Valavan, N Baskar, A Bovas Herbert Bejaxhin Tehnički vjesnik 31 (4), 1360-1366 , 2024 2024 Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
Prediction of surface roughness of end milling operation using genetic algorithm G Mahesh, S Muthu, SR Devadasan The International Journal of Advanced Manufacturing Technology 77, 369-381 , 2015 2015 Citations: 101
Sustainable Additive Manufacturing and Environmental Implications: Literature Review EMG M Gopal, HG Lemu Sustainability 15 (504), 1-23 , 2022 2022 Citations: 80
Analysis of Spectroscopic, Morphological Characterization and Interaction of Dye Molecules for the Surface Modification of TiB 2 Nanoparticles S Mayakannan, R Rathinam, R Saminathan, R Deepalakshmi, M Gopal, ... Journal of Nanomaterials 2022 (1), 1033216 , 2022 2022 Citations: 48
Influence of friction in simple upsetting and prediction of hardness distribution in a cold forged product R Ganesh Narayanan, M Gopal, A Rajadurai Journal of Testing and Evaluation 36 (4), 371-383 , 2008 2008 Citations: 36
A study of cooling time, copper reduction and effects of alloying elements on the microstructure and mechanical properties of SG iron casting during machining SPS Singh Sivam, A Rajasekaran, S RajendraKumar, K SathiyaMoorthy, ... Australian Journal of Mechanical Engineering 19 (1), 10-18 , 2021 2021 Citations: 29
Investigation of Mechanical Behavior and Microstructure Analysis of AA7075/SiC/B4C-Based Aluminium Hybrid Composites MG HassabAlla, Satishkumar, Srinivasa Rao, Chebolu, Capangpangan, Alguno Advances in Materials Science and Engineering, 1-10 , 2022 2022 Citations: 28
Blockchain-based physically secure and privacy-aware anonymous authentication scheme for fog-based vanets J Subramani, A Maria, AS Rajasekaran, F Al-Turjman, M Gopal IEEe Access 11, 17138-17150 , 2022 2022 Citations: 26
Manufacturing System Modeling and Performance Analysis of Mineral Water Production Line using ARENA Simulation MG Dereje G, Temesgen G, Gutu O International Journal of Engineering and Advanced Technology 9 (5), 312-317 , 2020 2020 Citations: 25
Minimization of surface roughness and temperature during turning of aluminum 6061 using response surface methodology and desirability function analysis EM Gutema, M Gopal, HG Lemu Materials 15 (21), 7638 , 2022 2022 Citations: 24
Modeling and simulation of friction stir welding process for AA6061-T6 aluminum alloy using finite element method MG Muleta Tiki, Endalkachew Mosisa Engineering Solid Mechanics 10 (2), 139-152 , 2022 2022 Citations: 22
Effect of Machining Parameters and Optimization of Temperature Rise in Turning Operation of Aluminium-6061 Using RSM and Artificial Neural Network M Gopal Periodica Polytechnica Mechanical Engineering , 2021 2021 Citations: 22
Friction stir-welding of AZ31B Mg and 6061-T6 Al alloys optimization using Box-Behnken design (BBD) and Artificial Neural network (ANN) DA Efa, EM Gutema, HG Lemu, M Gopal Res. Eng. Struct. Mater. 10 (xxxx), 1-18 , 2023 2023 Citations: 19
A hybrid approach of NSGA-II and TOPSIS for minimising vibration and surface roughness in machining process N Zeelanbasha, V Senthil, G Mahesh International Journal of Operational Research 38 (2), 221-254 , 2020 2020 Citations: 19
Prediction of surface roughness in turning of duplex stainless steel (DSS) using response surface methodology (RSM) and artificial neural network (ANN) M Gopal Materials Today: Proceedings , 2021 2021 Citations: 18
The Hybrid Pareto Chart and FMEA methodology to Reduce Various Defects in Injection Molding Process NDD Mahesh Gopal Solid State Technology 64 (2), 3541-3555 , 2021 2021 Citations: 17
Alkali Treated Maize Fibers Reinforced with Epoxy Poly Matrix Composites P Baranitharan, G Mahesh Magnesium 15 (30), 150 , 2014 2014 Citations: 17
Temperature optimization by using response surface methodology and desirability analysis of aluminium 6061 EM Gutema, M Gopal, HG Lemu Materials 15 (17), 5892 , 2022 2022 Citations: 16
Optimization of CO2 laser drilling process parameters of GFRP/Al2O3/perlite composites GG Mahesh, J Kandasamy Materials Today Communications 35, 105962 , 2023 2023 Citations: 14
Genetic variability, heritability, genetic advance and path coefficients for grain protein content, quality traits and grain yield in rice (Oryza sativa L.) germplasm lines G Mahesh, T Ramesh, S Narendar Reddy, A Meena, S Rathod, RA Fiyaz, ... The Pharma Innovation Journal 11 (3), 1836-1839 , 2022 2022 Citations: 14
Hirsutella N Reddy, G Mahesh, M Priya, RUS Singh, L Manjunatha Beneficial microbes in agro-ecology, 817-831 , 2020 2020 Citations: 14
