Ph. D. - Symbiosis International University (2011-2014)
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Industrial and Manufacturing Engineering, Metals and Alloys, Surfaces, Coatings and Films
48
Scopus Publications
834
Scholar Citations
15
Scholar h-index
19
Scholar i10-index
Scopus Publications
Machine learning driven optimisation of mechanical and microstructural behaviour in FFF-printed Onyx–HSHT composites Bhagyashri Hiralal Dhage, Nitin K. Khedkar, Vijayshri Khedkar Journal of Materials Science Materials in Engineering, 2026 Additive Manufacturing (AM) is a transformative technology that allows the construction of lightweight, high-strength composite structures with customised geometries, making it mostly suitable for aerospace, defence, and structural applications. This study examines the mechanical behaviour of Onyx–High-Strength High-Toughness (HSHT) fibre composites fabricated using Fused Filament Fabrication (FFF). It establishes an integrated optimisation outline combining experimental testing, statistical analysis, machine learning (ML), and scanning electron microscopy (SEM). Tensile, flexural, and impact tests revealed significant adaptability in performance, with tensile strength of 177.50 MPa, flexural strength of 68.9 MPa, and impact energy of 7.487 J. Meanwhile, Analysis of Variance (ANOVA) established substantial sensitivity of mechanical properties to processing parameters, with F-values of 21.54, 70.39, and 294.15, respectively. Predictive ML models, predominantly the Random Forest Regressor, revealed high accuracy (R 2 up to 0.996), and interactive linear regression effectively modelled flexural strength. SEM analysis explained critical microstructural factors, such as void morphology and fibre–matrix interface quality, that influence mechanical performance. The combined DOE–ANOVA–ML–SEM approach provides a robust, data-driven methodology for the practical prototyping and optimisation of continuous fibre-reinforced AM composites, enabling accelerated design and improved performance prediction of advanced composite materials for practical engineering applications.
Predictive modeling and experimental validation of mechanical–microstructural relationships in 3D-printed Onyx–fibre composites Bhagyashri Hiralal Dhage, Nitin K. Khedkar, Mithul J. Naidu, Sachin Salunkhe, Lenka Cepova, Emad Abouel Nasr Scientific Reports, 2026 Additive Manufacturing (AM) has emerged as a revolutionary fabrication technology for producing high-performance polymer-based composite materials. Fused Filament Fabrication (FFF), a leading AM technology, facilitates the fabrication of continuous fibres in a thermoplastic matrix to provide a customised mechanical response. In this study, the tensile and flexural properties of Onyx matrix composites reinforced with continuous carbon fibre (CF) and glass fibre (GF) are investigated. Specimens were fabricated using a Markforged 3D printer and tested in accordance with ASTM standards. A Taguchi L27 design was employed to examine the effect of infill density, fibre volume fraction, fibre orientation, and roof and floor layers. The principal results indicated maximum tensile strengths of 286.8 MPa (CF) and 183.2 MPa (GF), and a flexural strength of 208.4 MPa (CF). ANOVA indicated the significance of the variables (high F-values, low residuals), especially in the CF flexural and GF tensile models. The regression analysis provided high predictive accuracy for all models, with predicted R-squared values of 98.73% (CF flexural), 97.69% (GF tensile), 91.04% (GF flexural), and 84.22% (CF tensile). Separate machine learning models trained on experimental data further predicted mechanical properties: a linear regression model provided R 2 = 99.49% for flexural data, while a multi-output random forest model predicted tensile strength and elongation with R 2 = 99.38% and 96.95%, respectively. SEM analysis of the fractured specimens validated failure modes, such as fibre pullout, matrix cracking, and delamination in CF (brittle) compared to ductile debonding in GF. The integrated Taguchi-ANOVA, machine learning, and microstructural analysis offers a robust platform for optimising the performance of FFF-fabricated fibre-reinforced Onyx composites.
Experimental Investigation of Surface Integrity Analysis Using Machine Learning for Nano-Powder Mixed Electrical Discharge Machining Amreeta R. Kaigude, Nitin K. Khedkar, Vijaykumar S. Jatti Journal of Manufacturing and Materials Processing, 2026 This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO2) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response Surface Methodology (RSM) to systematically analyze the machining of AISI D2 tool steel using copper electrodes. The study examined five critical process parameters, gap current (Ip), pulse-on duration (Ton), pulse-off time (Toff), gap voltage (V), and powder concentration, evaluating their combined effects on surface roughness (SR), surface crack density (SCD), and residual stress characteristics. Advanced characterization techniques including scanning electron microscopy (SEM) were employed to analyze surface topography and subsurface microstructural changes. The optimization process successfully identified optimal machining conditions of current = 9 A, Ton = 100 µs, Toff = 10 µs, and gap voltage = 65 V, achieving exceptional surface quality with a minimum surface roughness of 3.22 µm. Remarkably, these optimized parameters resulted in crack-free surfaces with zero surface crack density and minimal residual stress values across the 2θ range of 90° to 180°. To enhance predictive capabilities, supervised machine learning algorithms were implemented to model surface roughness behavior. Comparative analysis of classification algorithms demonstrated that Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), and Gaussian Naïve Bayes achieved superior performance with F1-scores of 0.88 and prediction accuracies of 90%. The integration of sustainable Jatropha biodielectric with TiO2 nanoparticles represents a significant advancement in environmentally conscious precision machining, while the machine learning approach establishes a robust framework for intelligent process optimization and quality prediction in advanced manufacturing applications.
Predictive machine learning and printing parameter optimization for enhanced impact performance of 3D-printed Onyx-Kevlar composites Bhagyashri Hiralal Dhage, Nitin K. Khedkar Discover Materials, 2025 Additive manufacturing enables the design of lightweight, high-performance composites customised for specific mechanical applications. This study aims to enhance the impact resistance of 3D-printed Onyx-Kevlar fiber-reinforced composites by optimizing printing parameters and predictive machine learning (ML) modeling. A Taguchi L27 orthogonal array was used to systematically examine the influence of fiber orientation, volume fraction, infill density, and pattern on impact performance. Statistical analysis identified fiber volume fraction and infill density as the most significant factors. To reduce experimental load and enable accurate property estimation, supervised ML models, Support Vector Regression (SVR), and Linear Regression, were developed and trained on experimental data. Both models demonstrated high predictive performance, with R² values of 0.9166 and 0.9747, respectively. Model accuracy was further validated through learning curves, residual analysis, and performance metrics. Scanning Electron Microscopy (SEM) provided microstructural insights into fracture behavior, correlating with model predictions. he combined approach provides a reliable and efficient method to optimize composite performance with less trial-and-error experimentation. This study demonstrates the utility of predictive ML in guiding material design and highlights its applicability in developing impact-resistant composites for protective and structural engineering applications.
Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi Methodsx, 2025 Activated Metal Inert Gas (A-MIG) welding of EN10028 (SA516Gr70) steel was investigated using varying current, voltage, and gas flow rate to assess their influence on tensile strength (TS). A total of 100 welded samples were prepared and tested per ASTM standards. Material characterization was performed on samples with the highest and lowest TS to evaluate the correlation between microstructure and strength. Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters. • The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %. • The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications.
Finite Element Analysis of CM247LC Superalloy for Gas Turbine Blade Application Tejan Chavan, Nitin Khedkar Engineering Technology and Applied Science Research, 2025 The objective of this article is to conduct a comparative analysis of the various materials used in the production of gas turbine blades. The materials under investigation include CM247LC, Nimonic 80A, and Inconel 738. The selected blade materials are required to demonstrate exceptional resistance to high temperatures and corrosion. It is determined that the most appropriate material for the construction of a gas turbine blade is a nickel-based superalloy. For the purposes of Finite Element Analysis (FEA), the aforementioned materials are defined as nickel-based superalloys. A comprehensive analysis of these materials was conducted using the ANSYS 2024 R2 student edition and a combination of structural and vibrational analyses was carried out. The deformation observed in CM247LC and Nimonic 80A exhibited nearly identical values of 0.965 mm and 0.884 mm, respectively. The results of the vibrational analysis indicated that all materials successfully circumvented the natural frequency as well as the operational natural frequency of 50 Hz, thereby ensuring the safe operation of the gas turbine blade. The findings demonstrated that the CM247LC satisfied both criteria for material selection, making it the most suitable material for gas turbine blade applications when compared to alternative materials. This is due to its comparatively lower deformation despite experiencing a greater magnitude of centrifugal force.
Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning Amreeta R. Kaigude, Nitin K. Khedkar, Vijaykumar S. Jatti, Sachin Salunkhe, Robert Cep, Emad Abouel Nasr Scientific Reports, 2024 Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.
Digital twin and its applications Kiran Wani, Nitin Khedekar, Varad Vishwarupe, N. Pushyanth Research Trends in Artificial Intelligence Internet of Things, 2023
Surface modification of die steel materials machined by powder mixed electrical discharge machining: A review International Journal of Applied Engineering Research, 2015
Thermal analysis of electromagnetic launcher using numerical simulation International Journal of Applied Engineering Research, 2015
Simulation of electromagnetic launcher structure using finite element method International Journal of Applied Engineering Research, 2015
Optimization of turning process parameters for surface roughness and MRR based on the Taguchi method during machining of Inconel-718 International Journal of Applied Engineering Research, 2014
Product detailing, a key to implementation of product design concepts for sustainable design Arpn Journal of Engineering and Applied Sciences, 2014
Surface improvement of H13 hot die steel material by EDM method using silicon carbide powder-mixed dielectric International Journal of Applied Engineering Research, 2014
RECENT SCHOLAR PUBLICATIONS
Machine learning driven optimisation of mechanical and microstructural behaviour in FFF-printed Onyx–HSHT composites BH Dhage, NK Khedkar, V Khedkar Journal of Materials Science: Materials in Engineering 21 (1), 77 , 2026 2026
Predictive modeling and experimental validation of mechanical–microstructural relationships in 3D-printed Onyx–fibre composites BH Dhage, NK Khedkar, MJ Naidu, S Salunkhe, L Cepova, EA Nasr Scientific Reports , 2026 2026
Experimental Investigation of Surface Integrity Analysis Using Machine Learning for Nano-Powder Mixed Electrical Discharge Machining AR Kaigude, NK Khedkar, VS Jatti Journal of Manufacturing and Materials Processing 10 (4), 115 , 2026 2026
Prediction of Specific Wear Rate of Laser Powder Bed Fusion Manufactured Inconel 718 Material Using Different Supervised Machine Learning Algorithms. T Chavan, N Khedkar, V Khedkar Journal Européen des Systèmes Automatisés 58 (9) , 2025 2025
Predictive machine learning and printing parameter optimization for enhanced impact performance of 3D-printed Onyx-Kevlar composites B Hiralal Dhage, NK Khedkar Discover Materials 5 (1), 174 , 2025 2025 Citations: 7
Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models RU Kakade, N Khedkar, A Dalavi MethodsX, 103514 , 2025 2025 Citations: 1
Experimental study of EGR rate on gasoline–diesel and biofueled medium load stationary single-cylinder RCCI engine P Katare, VS Kumbhar, RB Tirpude, NK Khedkar, AS Dalkilic, CK Chan Journal of Thermal Analysis and Calorimetry 150 (12), 9549-9560 , 2025 2025 Citations: 1
An experimental investigation and machine learning predictions to enhance microhardness in powder mixed electrical discharge machining N Khedkar, D Sawant, V Gulia, VS Jatti, VS Jatti International Journal on Interactive Design and Manufacturing (IJIDeM) 19 (5 … , 2025 2025
Finite Element Analysis of CM247LC Superalloy for Gas Turbine Blade Application T Chavan, N Khedkar Engineering, Technology & Applied Science Research 15 (1), 19917-19924 , 2025 2025 Citations: 1
Machine learning-assisted pattern recognition algorithms for estimating ultimate tensile strength in fused deposition modelled polylactic acid specimens A Mishra, VS Jatti, E Messele Sefene, AV Jatti, AD Sisay, NK Khedkar, ... Materials Technology 39 (1), 2295089 , 2024 2024 Citations: 28
k-Nearest neighbor-based machine learning algorithm to predict the taper angle during abrasive water-jet machining of stainless steel DA Sawant, VS Jatti, NK Khedkar, VS Jatti, S Salunkhe Abrasive Water Jet Machining of Composites: Empirical and Analytical … , 2024 2024
Tensile strength analysis of additively manufactured CM 247LC alloy specimen by employing machine learning classifiers VS Jatti, DA Sawant, NK Khedkar, VS Jatti, S Salunkhe, M Pagáč, ... PloS one 19 (7), e0305744 , 2024 2024 Citations: 9
Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning AR Kaigude, NK Khedkar, VS Jatti, S Salunkhe, R Cep, EA Nasr Scientific Reports 14 (1), 9683 , 2024 2024 Citations: 29
Fracture cracks localization in machined H13 tool steel using computer vision algorithms A Mishra, VS Jatti, NK Khedkar Science Talks 9, 100292 , 2024 2024
Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study VS Jatti, S Tamboli, S Shaikh, NS Solke, V Gulia, VS Jatti, NK Khedkar, ... Frontiers in Materials 10, 1336837 , 2024 2024 Citations: 20
Digital Twin and Its Applications K Wani, N Khedekar, V Vishwarupe, N Pushyanth Research Trends in Artificial Intelligence: Internet of Things, 120-134 , 2023 2023 Citations: 2
Experimental, Modal, and Harmonic Response Analysis of a Chladni Plate at Ultrasonic Frequencies N Khedkar, K Wani, V Jatti, V Joshi Engineering, Technology & Applied Science Research 13 (6), 12289-12294 , 2023 2023 Citations: 1
Comparative analysis of ROS SLAM algorithms in an autonomous nursing mobile robot A Rane, B Srinivasan, O Kamble, P Nikam, P Talekar, S Sattenapalli, ... 7th IET Smart Cities Symposium (SCS 2023) 2023, 566-570 , 2023 2023
Parametric optimization of wear parameters of hybrid composites (LM6/B 4 C/fly ash) using Taguchi technique J Udaya Prakash, S Ananth, S Jebarose Juliyana, R Cep, N Khedkar, ... Frontiers in Mechanical Engineering 9, 1279481 , 2023 2023 Citations: 11
Chladni Plate and Chladni Patterns—A Research Review of Theory, Modelling, Simulation and Engineering Applications K Wani, N Khedkar, V Jatti, V Khedkar International Conference on Sustainable and Innovative Solutions for Current … , 2023 2023 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Investigation of the effect of built orientation on mechanical properties and total cost of FDM parts S Raut, VKS Jatti, NK Khedkar, TP Singh Procedia materials science 6, 1625-1630 , 2014 2014 Citations: 207
A case study on thermo-hydraulic performance of jet plate solar air heater using response surface methodology MM Matheswaran, TV Arjunan, S Muthusamy, L Natrayan, H Panchal, ... Case Studies in Thermal Engineering 34, 101983 , 2022 2022 Citations: 100
Mechanical properties of 3D-printed components using fused deposition modeling: optimization using the desirability approach and machine learning regressor VS Jatti, MS Sapre, AV Jatti, NK Khedkar, VS Jatti Applied System Innovation 5 (6), 112 , 2022 2022 Citations: 75
The effects of cryogenic treatment on cutting tools S Kumar, NK Khedkar, B Jagtap, TP Singh IOP conference series: materials science and engineering 225 (1), 012104 , 2017 2017 Citations: 49
Deep cryogenic treatment of AISI M2 tool steel and optimisation of its wear characteristics using Taguchi‘s approach S Kumar, M Nagraj, A Bongale, N Khedkar Arabian Journal for Science and Engineering 43 (9), 4917-4929 , 2018 2018 Citations: 45
Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning AR Kaigude, NK Khedkar, VS Jatti, S Salunkhe, R Cep, EA Nasr Scientific Reports 14 (1), 9683 , 2024 2024 Citations: 29
Machine learning-assisted pattern recognition algorithms for estimating ultimate tensile strength in fused deposition modelled polylactic acid specimens A Mishra, VS Jatti, E Messele Sefene, AV Jatti, AD Sisay, NK Khedkar, ... Materials Technology 39 (1), 2295089 , 2024 2024 Citations: 28
A review on effect of powder metallurgy process on mechanical and tribological properties of Hybrid nano composites OM Patil, NN Khedkar, TS Sachit, TP Singh Materials today: proceedings 5 (2), 5802-5808 , 2018 2018 Citations: 27
Effect of deep cryogenic treatment on the mechanical properties of AISI D3 tool steel S Kumar, M Nagaraj, A Bongale, NK Khedkar International Journal of Materials Engineering Innovation 10 (2), 98-113 , 2019 2019 Citations: 25
Influence of deep cryogenic treatment on dry sliding wear behaviour of AISI D3 die steel S Kumar, M Nagaraj, NK Khedkar, A Bongale Materials Research Express 5 (11), 116525 , 2018 2018 Citations: 23
Material migration and surface improvement of OHNS die steel material by EDM method using tungsten powder-mixed dielectric NK Khedkar, TP Singh, VS Jatti WSEAS Transactions on Applied and Theoretical Mechanics 9 (1), 161-166 , 2014 2014 Citations: 21
Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study VS Jatti, S Tamboli, S Shaikh, NS Solke, V Gulia, VS Jatti, NK Khedkar, ... Frontiers in Materials 10, 1336837 , 2024 2024 Citations: 20
Investigating the effect of cryogenic treatment of workpieces and tools on electrical discharge machining performance. VS Jatti, NK Khedkar, VS Jatti, P Dhall AIMS Materials Science 9 (6) , 2022 2022 Citations: 17
Machine learning based predictive modeling of electrical discharge machining of cryo-treated NiTi, NiCu and BeCu alloys VS Jatti, RB Dhabale, A Mishra, NK Khedkar, VS Jatti, AV Jatti Applied System Innovation 5 (6), 107 , 2022 2022 Citations: 16
Review of the effect of built orientation on mechanical Properties of metal-plastic composite parts fabricated by Additive Manufacturing Technique S Magar, NK Khedkar, S Kumar Materials Today: Proceedings 5 (2), 3926-3935 , 2018 2018 Citations: 15
Synthesis and mechanical characterisation of aluminium-based hybrid nanocomposites reinforced with nano tungsten carbide and nano tantalum niobium carbide particles TS Sachit, M Nagaraj, A Bongale, N Khedkar International Journal of Materials Engineering Innovation 9 (4), 279-290 , 2018 2018 Citations: 14
Experimental and static numerical analysis on bumper beam to be proposed for Indian passenger car NK Khedkar, CR Sonawane, S Kumar Materials Today: Proceedings 42, 383-387 , 2021 2021 Citations: 13
Parametric optimization of wear parameters of hybrid composites (LM6/B 4 C/fly ash) using Taguchi technique J Udaya Prakash, S Ananth, S Jebarose Juliyana, R Cep, N Khedkar, ... Frontiers in Mechanical Engineering 9, 1279481 , 2023 2023 Citations: 11
Influence of deep cryogenic cooling on tool wear and surface roughness of coated tungsten carbide inserts using statistical techniques P Jadhav, S Kumar, A Bongale, N Khedkar Materials Research Express 6 (7), 076517 , 2019 2019 Citations: 11
Tensile strength analysis of additively manufactured CM 247LC alloy specimen by employing machine learning classifiers VS Jatti, DA Sawant, NK Khedkar, VS Jatti, S Salunkhe, M Pagáč, ... PloS one 19 (7), e0305744 , 2024 2024 Citations: 9
