Dr.Raviraj Shetty

Scopus Publications

Deep learning-driven optimization and predictive modeling of LASER beam machining for XG3 steel
Adithya Hegde, Raviraj Shetty, Gururaj Bolar, V Balaji
Scientific Reports, 2026
LASER Beam Machining (LBM) has emerged as a highly precise and non-contact thermal machining process, widely adopted for cutting complex geometries in advanced engineering materials. Its ability to machine difficult-to-cut alloys with minimal mechanical stress makes it particularly suitable for aerospace and defense components. This paper presents an experimental investigation and multi-objective optimization of LASER Beam Machining (LBM) for XG3 steel, a high-performance alloy used in aerospace and defense applications. The study evaluates the impact of four process parameters i.e. cutting speed (8, 10, 12 m/min), gas pressure (0.5, 0.7, 0.9 Bar), focus point (2, 4, 6 mm), and depth of cut (3, 6, 9 mm) on four output responses: surface roughness, machining time, surface hardness, and burr thickness. Experiments were conducted using a Taguchi L 27 orthogonal array on three distinct hole geometries: circular, triangular, and square. Analysis of Variance (ANOVA) revealed that cutting speed was the most dominant factor, contributing over 82% to the variation in surface roughness, 74% for machining time, 81% for surface hardness, and 84% for burr thickness. The interaction between cutting speed and depth of cut was also found to be statistically significant. For single-objective optimization, the ideal parameters to minimize surface roughness were a cutting speed of 12 m/min, gas pressure of 0.5 bar, focus point of 2 mm, and depth of cut of 3 mm. Multi-objective optimization using a Genetic Algorithm (MOGA) generated Pareto fronts to identify balanced trade-off solutions; for a circular profile, this resulted in surface roughness values of 1.10–1.16 μm and machining times of 2.44–2.52 s. Furthermore, two predictive models, Response Surface Methodology (RSM) and a Back-Propagation Artificial Neural Network (BPANN), were developed. Comparative analysis showed the BPANN model was significantly more accurate, with regression coefficients (R) exceeding 0.999 and Mean Absolute Percentage Error (MAPE) values of 1.48% for surface roughness and 0.72% for surface hardness, confirming its superior predictive capability.
A rod-inserted design approach to delay failure and improve Mode II fracture toughness in metallic cylinders
Mohan Kumar, Neelakantha V Londhe, Adithya Hegde, Ramachandra CG, Raviraj Shetty
Applications in Engineering Science, 2026
Statistical Multi-Response Optimization and Prediction of Abrasive Water Jet Machining Process Parameters for HRS Fiber/CNT/Epoxy Hybrid Composites
Supriya P, Raviraj Shetty, Gururaj Bolar, Rajesh Nayak, Sawan Shetty, Adithya Hegde
Journal of Composites Science, 2026
This paper investigates the AWJ machinability of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) fiber/epoxy-based hybrid composites by analyzing key machinability metrics such as kerf width (KW), material removal rate (MRR), and surface roughness (Ra). Various process parameters including CNT weight percentage, CNT diameter, stand-off distance, and traverse speed have been varied to optimize the machining performance. Experimental analysis suggested that increasing the CNT weight percentage significantly enhanced material hardness, thereby reducing both the MRR and surface roughness. Moreover, adjusting the stand-off distance and traverse speed further improved the machinability of the composite. ANOVA results highlighted that CNT weight percentage was a significant factor, accounting for 94.17% of the variation in MRR and 93.72% of the variation in surface finish, while the stand-off distance influenced 87.03% of the variation in kerf width. Additionally, response surface methodology (RSM) was utilized to develop predictive models that estimated KW, MRR, and Ra with error rates of 2.95%, 2.23%, and 5.65%, respectively. These insights offer a valuable framework for tailoring the AWJ process to achieve optimal machining outcomes in HRS/CNT/epoxy composite materials
Thermal and mesomorphic behavior of hydrogen-bonded liquid crystals of new pyridine derivatives
Suma Ramachandra Gopady, V. N. Vijayakumar, Raviraj Shetty, Mallikarjun Bhavanari, Srinivasulu Maddasani
Discover Applied Sciences, 2026
The fascinating supramolecular structures formed through intermolecular hydrogen bonding (HB) interactions have gained importance in materials and biological science research. Pyridine-infused molecular units are more prone to form HB interactions with other moieties, such as carboxylic acids, alcohols, and thiols. In this study, new mesomorphic materials with large thermal spans and towards ambient temperatures were synthesized through hydrogen bonding interactions. New pyridine derivative of Schiff’s base with dihalo substituents was prepared and used to form intermolecular HB interactions with 4- n -alkyloxybenzoic acids ( n OBAs). The 4- n -alkyloxybenzoic acids ( n OBAs) are inherently mesomorphic, whereas the new pyridine derivative is non-mesogenic. The 1:1 binary mixtures of pyridine derivative and n OBAs were found to involve HB interactions, and the inherent mesomorphism of the n OBAs varied. The nematic and smectic-C mesophases of n OBAs are quenched, and the smectic-A mesophase is induced at ambient temperatures. Infrared spectroscopy was used to ascertain the intermolecular HB interactions and polarizing optical microscopy (POM) in conjunction with a temperature controller and differential scanning calorimetry (DSC) were used to ascertain the mesomorphism and phase transition temperatures, respectively. The mesomorphic thermal spans were found to be stabilized in the present study as the chain length of carboxylic acids increased. The melting and clearing temperatures of the complexes were lower than those of the n OBAs. This research promotes knowledge-sharing and multi-stakeholder collaboration, aligning with SDG 17 objectives
Multi-Response Optimization of Thermal Conductivity and Rheological Behavior in Nanoparticle-Enhanced Vegetable Oil Emulsions
Vishal Shenoy P, Vijay Kini M, Raghuvir Pai B, Srinivas Shenoy Heckadka, Raviraj Shetty, Supriya J. P, Adithya Hegde
Journal of Composites Science, 2026
In metal cutting industries, optimizing the thermal conductivity and viscosity of vegetable oil-based cutting fluids is critical for ensuring efficient heat dissipation, effective lubrication, and sustainability, directly influencing tool life and machining performance. This study presents a comprehensive experimental analysis employing statistical methods, particularly Taguchi’s Design of Experiments, to evaluate the thermal conductivity and viscosity of Pongamia pinnata, sunflower, and coconut oil incorporated with Silicon Dioxide (SiO2), Hexagonal Boron Nitride (hBN), and Cupric Oxide (CuO) nanoparticles across different emulsion ratios and nanoparticle volume fractions. The results revealed that Pongamia pinnata oil containing 0.5 (Vol.%) SiO2 nanoparticles at an emulsion ratio of 1:7 achieved the maximum thermal conductivity, measured at 0.637 W/mK. Additionally, the results revealed that Pongamia pinnata oil at an emulsion ratio of 1:13 exhibited the highest viscosity of 1.33 mPa·S, confirming that both the type of cutting oil and the emulsion ratio are the primary factors influencing viscosity. Further, the ANOVA analysis for thermal conductivity and viscosity highlights that the type of cutting fluid is the dominant factor, accounting for 90.58% of the total variance in thermal conductivity and 70.47% in viscosity, each with a highly significant p-value of 0.00, underscoring its decisive impact on the stability of both properties. Overall, this research offers important guidance for the selection and formulation of vegetable oil-based emulsions with nanoparticle additives. The results support the development of advanced nano lubricants with enhanced performance, catering to the increasing requirements of diverse industrial applications.
Multi-scale wear analysis of HRS/CNT hybrid polymer composites using predictive statistical modelling techniques
Sawan Shetty, Supriya J. P., Raviraj Shetty, Rajesh Nayak, Gururaj Bolar, Adithya Hegde
Cogent Engineering, 2026
This study investigates the mechanical properties of Hibiscus Rosa-Sinensis/carbon nanotube (CNT) hybrid polymer composites, aiming to optimize specific wear rate and worn surface hardness under varying applied loads, rotational speeds, and CNT weight percentages. ANOVA statistical analysis revealed that the CNT weight percentage is the most critical factor, accounting for over 55% of the variation in both wear rate and hardness. Scanning Electron Microscopy confirmed that a 3 wt.% CNT composition provides the highest wear resistance and surface integrity across all tested loads. Furthermore, Response Surface Methodology (RSM) accurately predicted these wear behaviors, showing average errors of less than 3% for specific wear rate and under 7% for worn surface hardness. By utilizing desirability function analysis, this research provides a highly reliable framework for optimizing hybrid composites, paving the way for advanced materials that meet industrial demands for durability and performance.
A systematic analysis on the electrospinnability of biocompatible poly(butylene adipate-co-terephthalate)
Animita Das, S Anandhan, Chethan K N, Sampath Suranjan Salins, Raviraj Shetty, Sawan Shetty
Engineering Research Express, 2025
Fine-tuning electrospun nanofibers is crucial for producing high-quality fibers. Taguchi Design of Experiment (DOE), along with various other computational techniques, has been used to optimize the electrospinning parameters of different polymers. Taguchi DOE has proven effective in optimizing electrospun nanofibers because it reduces the number of trials needed. In this study, the electrospinning parameters of poly (butylene adipate- co -terephthalate) (PBAT) were optimized and quantified using the Taguchi-based Response Surface Methodology (RSM) approach. The average fiber diameters were measured from Field Emission Scanning Electron Microscopy (FESEM) images using ImageJ software. Within the tested range of parameters and levels, the Analysis of Variance (ANOVA) study identified polymer concentration and flow rate as the most significant factors that influenced the fiber diameter. Polymer concentration accounting 56.94% of the variation, while Flow Rate (FR) accounts for 20.82%. The optimal parameter levels were predicted to be 10 wt% polymer concentration, 1 ml h −1 flow rate, 18 kV voltage, and a distance from tip to target of 15 cm, which yielded fibers with an average diameter of 231 nm and an accuracy of 88.61%. Overall, the results demonstrate that Taguchi DOE, coupled with RSM, is a reliable and efficient method for identifying the optimal parameter combinations to produce uniform, fine PBAT nanofibers intended for biomedical applications.
A synergistic approach for thermal characteristic analysis of hibiscus rosa-sinensis/carbon nano-tubes/epoxy based hybrid composites using soft computing techniques
Supriya J P, Raviraj Shetty, Sawan Shetty, Rajesh Nayak, Gururaj Bolar, Adithya Hegde
Materials Research Express, 2025
This study examines the thermal properties and combustion behavior of HRS/CNT hybrid polymer composites, focusing on how variations in CNT weight percentage, CNT diameter, sonication time, and composite thickness affect thermal conductivity, degradation temperature, and burning rate. The experimental results demonstrate that a CNT weight percentage of 3% significantly enhances thermal conductivity, achieving a maximum value of 0.985 W m −1 ·K, while also elevating the thermal degradation temperature to a peak of 457 °C. These enhancements are due to the effective formation of a CNT network that facilitates efficient heat transfer and increases thermal stability. Larger CNT diameters, specifically 3 nm, contribute to improved thermal and flame-retardant properties, resulting in a reduced burning rate of 5.18 mm min −1 compared to 11 mm/min for composites with 1% CNT and 1 nm diameter. Optimal sonication time at 60 min ensures uniform CNT dispersion within the polymer matrix, further enhancing thermal conductivity and reducing burning rates. Additionally, thicker composites (20 mm) exhibit lower burning rates and higher thermal degradation temperatures, serving as more effective thermal barriers. BPANN prediction is succesfully applied with a neglegible error of 0.15% compared with 0.68% error of RSM predictions. This research provides valuable insights into the design and optimization of polymer composites with tailored thermal and combustion properties, offering potential applications in fields requiring high thermal stability and flame resistance.
Computational optimization of 3D printed bone scaffolds using orthogonal array-driven FEA and neural network modeling
Amulya Shetty, Aamirah Fathima, B Anika, Raviraj Shetty, Vinyas, J.P. Supriya, Adithya Hegde
Scientific Reports, 2025
Today, orthopedic surgeons have been continuously focusing on bone tissue engineering for regenerating damaged bone through the use of biomimetic scaffolds and innovative materials. Hence, this study presents a comprehensive investigation into the optimization of PLA + 3D printed lattice scaffolds for bone tissue engineering applications, emphasizing the role of geometric configuration and processing parameters on mechanical performance. Three distinct lattice geometries such as Lidinoid, Diamond, and Gyroid were developed with varying wall thicknesses (1.0 mm, 1.5 mm, and 2.0 mm) and subjected to compressive loads of 3 kN, 6 kN, and 9 kN. A Taguchi L27 Orthogonal Array was employed to evaluate key mechanical responses, including displacement and strain. Among these configurations, the Gyroid lattice exhibited superior mechanical integrity, demonstrating the least displacement (0.36 mm) and strain (1.2 × 10⁻²) at 3 kN with 2.0 mm thickness, whereas the Lidinoid structure showed the highest deformability. A Back-propagation Artificial Neural Network (BPANN) model was developed to predict scaffold behavior with remarkable accuracy (R² = 0.9991 for displacement, R² = 0.9954 for strain), further Finite Element Analysis (FEA) was conducted to validate both experimental and predicted results. The novelty of this work lies in its integrative, multi-modal approach that synergizes experimental design, machine learning-based predictive modeling, and simulation. The focus of this study is to define a robust framework for optimizing scaffold architecture, with significant implications for enhancing mechanical strength and biological performance in bone healing applications.
Delamination and Its Morphological Study on Hibiscus Rosa-Sinensis/Carbon Nano-Tubes/Epoxy Based-Hybrid Composites During Abrasive Water-Jet Machining Using Statistical Optimization Techniques
Supriya J. P., Raviraj Shetty, Sawan Shetty, Rajesh Nayak, Adithya Hegde
Journal of Composites Science, 2025
The natural fiber-reinforced nanomaterial filler polymer matrix hybrid composite has superior applications in industrial and manufacturing fields due to its enhanced mechanical and machinability characteristics. However, in order to generate high-quality components, unconventional machining techniques, notably abrasive waterjet machining, have become more popular due to the inhomogeneity of composites, fiber pullout, greater surface roughness, and dimensional inaccuracy under traditional machining. Delamination typically refers to the separation that occurs along a plane parallel to the surface, such as the detachment of a coating from its underlying material or the separation between different layers within the coating itself. This paper investigates the AWJM characteristics of Hibiscus Rosa-Sinensis/Carbon nanotube/Epoxy (HRSCE)-based hybrid composite, focusing on delamination factors at entry, exit, and machining time. An L27 orthogonal array was employed to optimize process parameters, revealing that DF-entry decreased with increasing CNT (wt.%), achieving its lowest values at 3 (wt.%) CNT and 2 mm stand-off distance due to enhanced composite toughness and precise jet focus. Conversely, DF-exit increased with higher CNT (wt.%), stand-off distance and traverse speed, attributed to the composite’s increased brittleness and reduced cutting efficiency. Machining time was predominantly influenced by CNT (wt.%) (92.4%), increasing with higher reinforcement levels due to enhanced material resistance. Response surface methodology models demonstrated high accuracy in predicting machining outcomes, with errors below 3%. Contour and surface plots identified optimal conditions for minimal delamination and machining time as 3 (wt.%) CNT, low stand-off distance (2 mm), and moderate traverse speed (200 mm/min). The SEM and optimal microscopy analysis confirmed that CNT reinforcement positively influenced fiber matrix interfacial integrity and reduced surface damage.
Poly (butylene adipate-co-terephthalate) (PBAT) in biomedical applications: a comprehensive review of material properties, fabrication methods, and biofunctional potential
Animita Das, Chethan K N, Sampath Suranjan Salins, Raviraj Shetty, Sawan Shetty
Materials Research Express, 2025
Performance assessment and optimization of Ti6Al4V helical hole milling process
Gururaj Bolar, Vikas Marakini, Raviraj Shetty, Sawan Shetty, Adithya Hegde
Materials Research Express, 2025
Comprehensive analysis of drilling responses in additively manufactured PLA using a regression—based statistical learning approach
Vishwadarshan, Gauthami Shetty, Raviraj Shetty, Supriya J P, Balaji V, Adithya Hegde
Materials Research Express, 2025
Integrating soft computing approach to optimize and predict powder mixed wire electric discharge machinability characteristics of PBLF-Ti-6Al-4V alloy
Gururaj Bolar, Rajesh Nayak, Raviraj Shetty, Supriya J. P., Adithya Hegde
Cogent Engineering, 2025
Tribological wear analysis of HRS/CNT polymer hybrid composites using statistical optimization technique
J. P. Supriya, Gururaj Bolar, Raviraj Shetty, Rajesh Nayak, Sawan Shetty, Adithya Hegde
International Journal of System Assurance Engineering and Management, 2025
Titanium alloy and its composites: machinability review
Raviraj Shetty, Gary Anthony Gracias, Adithya Hegde, Shreyas Manoj Bagade, J. P. Supriya, V. Shashwat Raman
International Journal of System Assurance Engineering and Management, 2025
Machinability characteristics study on hibiscus rosa-sinensis reinforced polymer composites using soft computing techniques
Sawan Shetty, Raviraj Shetty, Rajesh Nayak, Supriya J P, Adithya Hegde
Engineering Research Express, 2024
Chemical characterization of hibiscus rosa-sinensis plant fibers facilitated through design of experiments and artificial neural network hybrid approach
J. P. Supriya, Raviraj Shetty, Nithesh Naik, Srinivasulu Maddasani, Adithya Hegde
Scientific Reports, 2024
Mechanical and physical characterization of chemically treated Hibiscus Rosa-Sinensis polymer matrix composites using deep learning and statistical approach
J P Supriya, Raviraj Shetty, Sawan Shetty, Gururaj Bolar, Adithya Hegde
Materials Research Express, 2024
Optimizing the Die-Sink EDM Machinability of AISI 316L Using Ti-6Al-4V-SiCp Electrodes: A Computational Approach
Adithya Hegde, Raviraj Shetty, Rajesh Nayak, Sawan Shetty, Uday Kumar Shetty SV
Journal of Manufacturing and Materials Processing, 2024
Quantitative and deep learning based fourier transform infrared radiation and tensile characteristics study on chemically treated hibiscus rosa-sinensis plant fibers
Supriya J P, Sawan Shetty, Raviraj Shetty, Rajesh Nayak, Srinivasulu Maddasani, Adithya Hegde
Materials Research Express, 2024
Performance Analysis of Helical Milling and Drilling Operations While Machining Carbon Fiber-Reinforced Aluminum Laminates
Gururaj Bolar, Anoop Aroor Dinesh, Ashwin Polishetty, Raviraj Shetty, Anupama Hiremath, V. L. Neelakantha
Journal of Manufacturing and Materials Processing, 2024
An integrated experimental and analytical approach on mechanical characterization of advanced powder metallurgy aluminium metal matrix composites reinforced with different particulates
Sawan Shetty, Raviraj Shetty, Navaneet Shetty, Adithya Hegde
Materials Research Express, 2024
Design of experiments integrated with neural networks for optimization and predictive modelling of electrode wear of novel Ti-6Al-4V-SiCp composites during die sinking electric discharge machining
Adithya Hegde, Raviraj Shetty, Rajesh Nayak, Sawan Shetty
Materials Research Express, 2024
Cryogenic Cooling Assisted Machining of Elastomers – A Critical Review
Malikasab Bagawan, Suresh T. Dundur, Rajesh Gurani, Raviraj Shetty, Adithya Lokesh Hegde
Aip Conference Proceedings, 2024
Orthogonal Array and Artificial Neural Network Approach for Cutting Force Optimization during Machining of Ti-6Al4V under Minimum Quantity Lubrication (MQL)
Madhukar Nayak, Sanjeev Kumar Chougula Ramappa, Raviraj Shetty, Adithya Lokesh Hegde, Devang Shetty
Aip Conference Proceedings, 2024
Optimization and Prediction of Cutting Force for Sustainable Turning of Ti-6Al-4V under Different Lubricant Viscosity Conditions
Chougula Ramappa Sanjeev Kumar, Raviraj Shetty, Rajesh Nayak, Adithya Lokesh Hegde, Devang Shetty, Madhukara Nayak
Aip Conference Proceedings, 2024
Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites
Adithya Hegde, Rajesh Nayak, Gururaj Bolar, Raviraj Shetty, Rakesh Ranjan, Nithesh Naik
Journal of Composites Science, 2024
Experimental investigation on the effect of rice straw reinforcement on the wear properties of polymer composite
Abhishek Sadananda Madival, Deepak Doreswamy, Raviraj Shetty
International Journal of Surface Science and Engineering, 2024
Electrospun nanofibers: transformative innovations in biomedical applications and Future prospects in healthcare advancement
Animita Das, Sawan Shetty, Chethan K. N., Raviraj Shetty, Sampath Suranjan Salins
Cogent Engineering, 2024
DOE coupled MLP-ANN for optimization of thrust force and torque during drilling of CCFRP composite laminates
Sawan Shetty, Raviraj Shetty, Rajesh Nayak, Adithya Hegde, Uday Kumar Shetty S. V., Sudheer M.
Cogent Engineering, 2024
RSM based expert system development for cutting force prediction during machining of Ti–6Al–4V under minimum quantity lubrication
R. Shetty, C. R. Sanjeev Kumar, M. R. Ravindra
International Journal of System Assurance Engineering and Management, 2023
Characterization and optimization of thermal properties of rice straw and Furcraea foetida fiber reinforced polymer composite for thermal insulation application
Abhishek Sadananda Madival, Raviraj Shetty, Deepak Doreswamy, Srinivasulu Maddasani
Journal of Building Engineering, 2023
Statistical and Artificial Neural Network Coupled Technique for Prediction of Tribo-Performance in Amine-Cured Bio-Based Epoxy/MMT Nanocomposites
Nithesh Naik, Ritesh Bhat, B. Shivamurthy, Raviraj Shetty, Parikshith R. Parashar, Adithya Lokesh Hegde
Journal of Composites Science, 2023
Optimization and Prediction of Process Parameters during Abrasive Water Jet Machining of Hybrid Rice Straw and Furcraea foetida Fiber Reinforced Polymer Composite
Abhishek Sadananda Madival, Deepak Doreswamy, Raviraj Shetty, Nithesh Naik, Prakash Rao Gurupur
Journal of Composites Science, 2023
Optimization and Prediction of Thermal Conductivity and Electrical Conductivity of Vacuum Sintered Ti-6Al-4V-SiC(15 Wt.%) Using Soft Computing Techniques
Adithya Hegde, Raviraj Shetty, Nithesh Naik, B. R. N. Murthy, Madhukar Nayak, Mohan Kumar, Deepika Shanubhogue
Journal of Composites Science, 2023
Tribological and Morphological Study of AISI 316L Stainless Steel during Turning under Different Lubrication Conditions
C. P. Natesh, Y. M. Shashidhara, H. J. Amarendra, Raviraj Shetty, S. R. Harisha, P. Vishal Shenoy, Madhukara Nayak, Adithya Hegde, Dikshith Shetty, Utsav Umesh
Lubricants, 2023
A review on the physical and mechanical properties of natural fiber reinforced composites and its machinability using abrasive waterjet machining
Abhishek Sadananda Madival, Deepak Doreswamy, Raviraj Shetty
Materials Today Proceedings, 2023
Characterization of Physical and Mechanical Properties of Rice Straw Particles and Furcraea foetida Fiber Reinforced Hybrid Composite
Abhishek Sadananda Madival, Deepak Doreswamy, Raviraj Shetty
Journal of Natural Fibers, 2023
Influence of Chemical Treatments on the Physical and Mechanical Properties of Furcraea Foetida Fiber for Polymer Reinforcement Applications
Abhishek Sadananda Madival, Srinivasulu Maddasani, Raviraj Shetty, Deepak Doreswamy
Journal of Natural Fibers, 2023
Dependence of pre-treatment structure on spheroidization and turning characteristics of AISI1040 steel
Harisha S R, Sathyashankara Sharma, Ramakrishna Vikas Sadanand, Achutha Kini U, Raviraj Shetty, Sathish Rao U
Cogent Engineering, 2023
Effect of metallic reinforcement and mechanically mixed layer on the tribological characteristics of Al-Zn-Mg alloy matrix composites under T6 treatment
Raviraj J Shetty, Jamaluddin Hindi, Gurumurthy B M, Ananda Hegde, Shivaprakash Y M, Sathyashankara Sharma, Amar Murthy A, Muralishwara K
Cogent Engineering, 2023
Soft computing approach for optimization of turning characteristics of elastomers under different lubrication conditions
Malikasab Bagawan, Suresh T. Dundur, Rajesh Gurani, Raviraj Shetty, Rajesh Nayak, S. V. Udaya Kumar Shetty, Madhukara Nayak, Adithya Hegde
Cogent Engineering, 2023
Soft computing for sustainable drilling of AISI 316L stainless steel under formulated neem oil minimum quantity lubrication condition
C.P. Natesh, Y.M. Shashidhara, H.J. Amarendra, Raviraj Shetty, Rajesh Nayak, S. V. UdayKumar Shetty, Madhukara Nayak, Adithya Hegde
Cogent Engineering, 2023
Influence of Thermal and Thermomechanical Stimuli on Dental Restoration Geometry and Material Properties of Cervical Restoration: A 3D Finite Element Analysis
Rohan Sharma Uppangala, Swathi Pai, Vathsala Patil, Komal Smriti, Nithesh Naik, Raviraj Shetty, Pranesh Gunasekar, Amritanshu Jain, Jeswanthi Tirupathi, Pavan Hiremath, Santosh Patil, Rashmitha Rathnakar
Journal of Composites Science, 2023
Processing and Mechanical Characterisation of Titanium Metal Matrix Composites: A Literature Review
Raviraj Shetty, Adithya Hegde, Uday Kumar Shetty SV, Rajesh Nayak, Nithesh Naik, Madhukar Nayak
Journal of Composites Science, 2022
Optimization and Prediction of Mechanical Characteristics on Vacuum Sintered Ti-6Al-4V-SiCp Composites Using Taguchi’s Design of Experiments, Response Surface Methodology and Random Forest Regression
Adithya Lokesh Hegde, Raviraj Shetty, Dundesh S Chiniwar, Nithesh Naik, Madhukara Nayak
Journal of Composites Science, 2022
Processing, Mechanical Characterization, and Electric Discharge Machining of Stir Cast and Spray Forming-Based Al-Si Alloy Reinforced with ZrO2 Particulate Composites
Raviraj Shetty, Prakash Rao Gurupur, Jamaluddin Hindi, Adithya Hegde, Nithesh Naik, Mohammed Sabraz Sabir Ali, Ishwargouda S. Patil, Madhukar Nayak
Journal of Composites Science, 2022
Processing, Characterization of Furcraea foetida (FF) Fiber and Investigation of Physical/Mechanical Properties of FF/Epoxy Composite
Abhishek Sadananda Madival, Deepak Doreswamy, Srinivasulu Maddasani, Manjunath Shettar, Raviraj Shetty
Polymers, 2022
Taguchi based fuzzy logic model for optimisation and prediction of surface roughness during AWJM of DRCUFP composites
Raviraj Shetty, Adithya Hegde
Manufacturing Review, 2022
Optimization and prediction of hardness, wear and surface roughness on age hardened stellite 6 alloys
Karthik S.R., Neelakanta V. Londe, Raviraj Shetty, Rajesh Nayak, Adithya Hedge
Manufacturing Review, 2022
L 1 6 Orthogonal Array-Based Three-Dimensional Finite Element Modeling for Cutting Force and Chip Formation Analysis during Dry Machining of Ti-6Al-4V
Raviraj Shetty, Sanjeev Kumar, Ravindra Mallagi, Laxmikanth Keni
Journal of Advanced Manufacturing Systems, 2021
A Study on the Effect of Radiometric Variations on A Fuzzy Stereo Matching Algorithm: A Statistical Analysis
Akhil Appu Shetty, , Navya Thirumaleshwar Hegde, Aldrin Claytus Vaz, C R Srinivasan, , , and
Engineered Science, 2021
The effect of SiC content in aluminum-based metal matrix composites on the microstructure and mechanical properties of welded joints
Jayashree PK, Gowrishankar MC, Sathyashankara Sharma, Raviraj Shetty, Pavan Hiremath, Manjunath Shettar
Journal of Materials Research and Technology, 2021
Performance analysis of a fuzzy disparity selector for stereo matching of image segments under radiometric variations
Akhil Appu SHETTY, Vadakekara Itty GEORGE, Chempi Gurudas NAYAK, Raviraj SHETTY
Turkish Journal of Electrical Engineering and Computer Sciences, 2020
Empirical study on stress distribution zone during machining of dracs using finite element analysis, taguchi's design of experiments and response surface methodology
Arpn Journal of Engineering and Applied Sciences, 2020
Multiple data cost-based stereo matching method to generate dense disparity maps from images under radiometric variations
Akhil Appu Shetty, V.I. George, C. Gurudas Nayak, Raviraj Shetty
International Journal of Intelligent Systems Technologies and Applications, 2020
A method for navigation of autonomous vehicles in outdoor environment through stereo correspondence
Akhil Appu Shetty
Journal of Advanced Research in Dynamical and Control Systems, 2020
Influence of homogenization and aging on tensile strength and fracture behavior of TIG welded Al6061-SiC composites
Jayashree PK, Gowrishankar MC, Sathyashankara Sharma, Raviraj Shetty, Manjunath Shettar, Pavan Hiremath
Journal of Materials Research and Technology, 2020
Mechanical and machinability study on discontinuously reinforced sisal fibre polyester composite laminates
Raviraj Shetty, Augustine B V Barboza, Rajesh Nayak
Materials Research Express, 2019
Disparity Maps Based Path Planning Algorithm for Autonomous Robot Navigation
Akhil Appu Shetty, V. I. George, C Gurudas Nayak, Raviraj Shetty
Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology Iceca 2019, 2019
Fuzzy logic-based disparity selection using multiple data costs for stereo correspondence
Akhil Appu SHETTY, V. I. GEORGE, C Gurudas NAYAK, Raviraj SHETTY
Turkish Journal of Electrical Engineering and Computer Sciences, 2019
Effect of post welded heat treatment on tungsten inert gas welded Al6061-SiC composites
P. Jayashree, Sathyashankara Sharma, R. Shetty
International Journal of Mechanical Engineering and Robotics Research, 2018
Processing, mechanical charaterization and its tribological study of discontinously reinforced Caryota Urens Fibre Polyester composites
Arpn Journal of Engineering and Applied Sciences, 2018
Statistical and surface metallurgical study during electric discharge machining of Ti-6Al-4V
Arpn Journal of Engineering and Applied Sciences, 2018
Low temperature machining of nitrile rubber
Nayak Rajesh, Nayak Akshay, Shetty Raviraj
International Journal of Automotive and Mechanical Engineering, 2018
Prediction of cutting force in turning of ti-6al-4v under minimum quantity lubrication (Mql) using response surface model and fuzzy logic model
Ravindra. R Malagi et al., Ravindra. R Malagi et al., and
International Journal of Mechanical and Production Engineering Research and Development, 2018
Quality enhancement of TIG Welded Al6061 SiCp composites by age hardening process
M. C. G. Shankar, Jayashree P. K, S. S. Sharma, R. Shetty, K. Vinay
International Journal of Automotive and Mechanical Engineering, 2018
Optimization of TIG welding parameters for 6061Al alloy using Taguchi's design of experiments
P.K. Jayashree, S.S. Sharma, Raviraj Shetty, Ashish Mahato, M.C. Gowrishankar
Materials Today Proceedings, 2018
Numerical investigation of chip formation during machining of elastomers and estimation of cutting force
Rajesh Nayak, Laxmikant G. Keni, Raviraj Shetty
International Review of Mechanical Engineering, 2017
Analysis of elastomer turning under different rake angles
Arpn Journal of Engineering and Applied Sciences, 2017
Effect of homogenizing and age hardening on mechanical property enhancement of TIG welded Al6061 SICp composites
P.K. Jayashree, Sathya Shankar Sharma, Shetty Raviraj
Materials Science Forum, 2017
Wear resistance enhancement of titanium alloy (Ti-6Al-4V) by ball burnishing process
Goutam Devaraya Revankar, Raviraj Shetty, Shrikantha Srinivas Rao, Vinayak Neelakanth Gaitonde
Journal of Materials Research and Technology, 2017
Soft computing techniques during drilling of bi-directional carbon fiber reinforced composite
Nagaraja Shetty, Mervin A. Herbert, Raviraj Shetty, Divakar S. Shetty, G.S. Vijay
Applied Soft Computing Journal, 2016
Normalized cross correlation for stereo matching under varying illumination
International Journal of Control Theory and Applications, 2016
Impact of process parameters on surface roughness in elastomer machining
International Journal of Applied Engineering Research, 2015
Mechanical characterization and evaluation of delamination of polymer composite laminate
International Journal of Applied Engineering Research, 2015
Analysis of surface roughness and hardness in ball burnishing of titanium alloy
Goutam D. Revankar, Raviraj Shetty, Shrikantha S. Rao, Vinayak N. Gaitonde
Measurement Journal of the International Measurement Confederation, 2014
Selection of optimal process parameters in ball burnishing of titanium alloy
Goutam D. Revankar, Raviraj Shetty, Shrikantha S. Rao, Vinayak N. Gaitonde
Machining Science and Technology, 2014
Analysis of surface roughness and hardness in titanium alloy machining with polycrystalline diamond tool under different lubricating modes
Goutam Devaraya Revankar, Raviraj Shetty, Shrikantha Srinivas Rao, Vinayak Neelakanth Gaitonde
Materials Research, 2014
Application of response surface methodology on surface roughness in grinding of aerospace materials (6061Al-15Vol%SiC25P)
Journal of Engineering and Applied Sciences, 2010
Experimental studies on turning of discontinuously reinforced aluminium composites under dry, oil water emulsion and steam lubricated conditions using TAGUCHI's technique
Gazi University Journal of Science, 2009
Taguchi's technique in machining of metal matrix composites
Raviraj Shetty, Raghuvir B. Pai, Shrikanth S. Rao, Rajesh Nayak
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2009
Steam as coolant and lubricant in turning of metal matrix composites
Raviraj Shetty, Raghuvir Pai, Vasanth Kamath, Shrikanth S. Rao
Journal of Zhejiang University Science A, 2008
A pressured steam JET approach to tool wear minimization in cutting of metal matrix composites
D. Anjaiah, Raviraj Shetty, R. Pai, M.V. Kini, S.S. Rao
Materials Science Forum, 2007

Dr.Raviraj Shetty

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