Research Publications
Browse peer-reviewed journal articles, conference papers, and scholarly publications. Use the filters below to refine results by type, author, year, or research area.
1.
Bharath, L.; Jayappa, J.
Microstructure Study and Optimization of Hardness and Tensile Strength of AW2024/B4Cp Reinforced Composites Through Linear Regression Journal Article
In: Mechanics of Advanced Composite Structures, vol. 13, pp. 83-96,, 2026, ISBN: 24234826 (ISSN); 24237043 (ISSN), (0).
@article{668,
title = {Microstructure Study and Optimization of Hardness and Tensile Strength of AW2024/B4Cp Reinforced Composites Through Linear Regression},
author = {L. Bharath and J. Jayappa},
url = {https://macs.semnan.ac.ir/article_9667.html},
doi = {10.22075/macs.2025.36248.1781},
isbn = {24234826 (ISSN); 24237043 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {Mechanics of Advanced Composite Structures},
volume = {13},
pages = {83-96,},
publisher = {Semnan University, Faculty of Mechanical Engineering},
abstract = {The current research is focused on the production of AW2024/B<inf>4</inf>C metal reinforced composite by using the liquid stir casting method. Ceramic particles armored composites are mainly used in engineering applications, which include aircraft, automotive, and marine fields. AW2024/B<inf>4</inf>C composites are produced by changing wt.% of B4Cp as 1.00%, 3.00% and 5.00%. The produced AW2024/B<inf>4</inf>C composites are machined as per ASTM E8-16a, IS1500, and IS7739 standard test size and subjected to artificial ageing. The hardness and tensile strength of AW2024/B<inf>4</inf>C composites were measured through a Brinell hardness tester and a universal testing machine, respectively. The microstructure of the prepared composite material was examined to determine the uniform distribution of reinforcement material. The highest hardness and tensile strength of AW2024/B<inf>4</inf>C composites were measured and found to be 84.97 BHN and 273.82 N/mm2 for AW2024/5%B<inf>4</inf>C with 5 hrs. ageing duration. The results achieved reveal that both hardness as well as tensile strength increased by increasing the weight percentage of B<inf>4</inf>C content. L<inf>9</inf> standard orthogonal display was espoused to investigate the best parameter and also to authenticate the experimental test results. Further, a fracture study was done through SEM images to determine the mode of fracture.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The current research is focused on the production of AW2024/B<inf>4</inf>C metal reinforced composite by using the liquid stir casting method. Ceramic particles armored composites are mainly used in engineering applications, which include aircraft, automotive, and marine fields. AW2024/B<inf>4</inf>C composites are produced by changing wt.% of B4Cp as 1.00%, 3.00% and 5.00%. The produced AW2024/B<inf>4</inf>C composites are machined as per ASTM E8-16a, IS1500, and IS7739 standard test size and subjected to artificial ageing. The hardness and tensile strength of AW2024/B<inf>4</inf>C composites were measured through a Brinell hardness tester and a universal testing machine, respectively. The microstructure of the prepared composite material was examined to determine the uniform distribution of reinforcement material. The highest hardness and tensile strength of AW2024/B<inf>4</inf>C composites were measured and found to be 84.97 BHN and 273.82 N/mm2 for AW2024/5%B<inf>4</inf>C with 5 hrs. ageing duration. The results achieved reveal that both hardness as well as tensile strength increased by increasing the weight percentage of B<inf>4</inf>C content. L<inf>9</inf> standard orthogonal display was espoused to investigate the best parameter and also to authenticate the experimental test results. Further, a fracture study was done through SEM images to determine the mode of fracture.
2.
Srimanickam, B.; Devi, Vandhana; Muralidharan, K.; Sarasu, P.; Srividhya, S.; Elangovan, K.; Krishnamoorthy, R.
In: 2025, ISBN: 1588-2926.
@article{622,
title = {Experimental and machine learning evaluation of a solar PVT system with water and Al2O3 nanofluids for improved electrical and thermal efficiency},
author = {B. Srimanickam and Vandhana Devi and K. Muralidharan and P. Sarasu and S. Srividhya and K. Elangovan and R. Krishnamoorthy},
url = {https://link.springer.com/article/10.1007/s10973-025-14589-8},
doi = {10.1007/s10973-025-14589-8},
isbn = {1588-2926},
year = {2025},
date = {2025-08-01},
abstract = {This study addresses the need for efficient and sustainable energy systems by investigating a solar photovoltaic-thermal (PVT) hybrid system for simultaneous electricity generation and hot water production, an approach crucial for residential applications in solar-rich regions. The novelty lies in combining experimental analysis with machine learning (ML) techniques to enhance and predict the system s performance, particularly using an Al2O3 nanofluid coolant, which has been rarely explored in prior literature. Experiments were conducted in Chennai, India, across winter months (December February) under varying flow rates (0.5 2.0 LPM) and two coolant types: water and a 0.1% vol. Al2O3 nanofluid. Key performance indicators, such as electrical efficiency, surface and tedlar temperatures, thermal output, and environmental conditions, were measured. To model and predict system behavior, four ML algorithms (Linear Regression, Random Forest, XGBoost, AdaBoost) were trained and evaluated using R2, mean absolute error (MAE), and root mean square error (RMSE). The XGBoost model outperformed others, achieving an R2 of 0.9726, MAE of 0.2411, and RMSE of 0.5437. Experimentally, the use of Al2O3 nanofluid improved electrical efficiency by up to 12.4% and thermal output by 18.7% compared to water. These findings demonstrate that integrating nanofluids and ML-based predictive analytics can significantly boost the efficiency of PVT systems, making them a viable option for sustainable domestic energy solutions in climates similar to that of Chennai.},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This study addresses the need for efficient and sustainable energy systems by investigating a solar photovoltaic-thermal (PVT) hybrid system for simultaneous electricity generation and hot water production, an approach crucial for residential applications in solar-rich regions. The novelty lies in combining experimental analysis with machine learning (ML) techniques to enhance and predict the system s performance, particularly using an Al2O3 nanofluid coolant, which has been rarely explored in prior literature. Experiments were conducted in Chennai, India, across winter months (December February) under varying flow rates (0.5 2.0 LPM) and two coolant types: water and a 0.1% vol. Al2O3 nanofluid. Key performance indicators, such as electrical efficiency, surface and tedlar temperatures, thermal output, and environmental conditions, were measured. To model and predict system behavior, four ML algorithms (Linear Regression, Random Forest, XGBoost, AdaBoost) were trained and evaluated using R2, mean absolute error (MAE), and root mean square error (RMSE). The XGBoost model outperformed others, achieving an R2 of 0.9726, MAE of 0.2411, and RMSE of 0.5437. Experimentally, the use of Al2O3 nanofluid improved electrical efficiency by up to 12.4% and thermal output by 18.7% compared to water. These findings demonstrate that integrating nanofluids and ML-based predictive analytics can significantly boost the efficiency of PVT systems, making them a viable option for sustainable domestic energy solutions in climates similar to that of Chennai.
3.
Basavaraj, Kusammanavar; Elangovan, K.; Kulkarni, Anand; Satyanarayan,
Experimental Analysis of Stability of Copper Oxide (CuO) Nanofluids Using Sedimentation Method Proceedings
Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-6107-7.
@proceedings{620,
title = {Experimental Analysis of Stability of Copper Oxide (CuO) Nanofluids Using Sedimentation Method},
author = {Kusammanavar Basavaraj and K. Elangovan and Anand Kulkarni and Satyanarayan},
url = {https://link.springer.com/chapter/10.1007/978-981-96-6107-7_15},
doi = {10.1007/978-981-96-6107-7_15},
isbn = {978-981-96-6107-7},
year = {2025},
date = {2025-07-01},
journal = {Novel Materials and Technologies for Energy and Environment Applications, Volume 1},
pages = {253-268,},
publisher = {Springer Nature Singapore},
address = {Singapore},
abstract = {Nanofluids are base fluids in which nanoscale particles are colloidal suspended. The research in the field of nanofluids becomes a hot topic due to their distinctive potential in applications involving heat transmission. The preparations and nanofluids stability are keen interesting topic for different applications of stable nanofluids. In this paper, CuO-DI water and CuO ethylene glycol (EG) nanofluids were made without adding dispersant using two-step method, in which CuO nanoparticles (20 nm sized) with varying concentrations from 0.01, 0.1, 0.5, 0.8, and 1% by volume were used. The magnetic stirrer with hot plate at temperatures of 35 C and 700 rpm was maintained for uniform distribution of nanoparticles contained in the base fluid and also does not allow the nanoparticles to aggregate. The nanofluid s stability was ascertained using the sedimentation method. The synthesized CuO-based nanofluid sample was observed for the 5 days and the nanofluid s stability was determined for the given sample. It was discovered that nanofluid concentrations greater than lower ones were less stable.},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Nanofluids are base fluids in which nanoscale particles are colloidal suspended. The research in the field of nanofluids becomes a hot topic due to their distinctive potential in applications involving heat transmission. The preparations and nanofluids stability are keen interesting topic for different applications of stable nanofluids. In this paper, CuO-DI water and CuO ethylene glycol (EG) nanofluids were made without adding dispersant using two-step method, in which CuO nanoparticles (20 nm sized) with varying concentrations from 0.01, 0.1, 0.5, 0.8, and 1% by volume were used. The magnetic stirrer with hot plate at temperatures of 35 C and 700 rpm was maintained for uniform distribution of nanoparticles contained in the base fluid and also does not allow the nanoparticles to aggregate. The nanofluid s stability was ascertained using the sedimentation method. The synthesized CuO-based nanofluid sample was observed for the 5 days and the nanofluid s stability was determined for the given sample. It was discovered that nanofluid concentrations greater than lower ones were less stable.
4.
Abel, Sunil; Amirtharajan, Saranya; Mahalingam, Arulprakasajothi; Baskaran, Srimanickam; Pandurengan, Sakthivel
In: vol. 10, pp. 100885+, 2025, ISBN: 2667-0224.
@article{618,
title = {Green synthesis of rGO/MgO nanocomposite using Hylocereus polyrhizuspeel extract for antibacterial activity and photocatalytic dye degradation study},
author = {Sunil Abel and Saranya Amirtharajan and Arulprakasajothi Mahalingam and Srimanickam Baskaran and Sakthivel Pandurengan},
url = {https://www.sciencedirect.com/science/article/pii/S2667022425000726},
doi = {10.1016/j.chphi.2025.100885},
isbn = {2667-0224},
year = {2025},
date = {2025-06-01},
volume = {10},
pages = {100885+},
abstract = {Current study demonstrates the preparation of rGO/MgO nanocomposite using Hylocereus polyrhizus due to its functional benefits, and green strategy method. Scanning electron microscopy (SEM) with Energy Dispersive X-ray analysis and X-ray diffraction were used to find the presence of crystal structure and the elemental analysis. XRD results revealed the cubic structure of the synthesized nanocomposite. The average crystallite size was 36.16 nm. The SEM portrays the agglomerated granular structure has larger scale web like structure are highly interconnected with large voids. The presence of graphene, magnesium and oxygen were analysed from the EDAX elemental studies. The presence of MgO and rGO diffraction planes is well matches with JCPDS card no. 04-0829 and 75-2078. Mg O C stretching vibrations and C O stretching vibrations reflected from FTIR studies confirms the presence of Mg and C in prepared composite. Furthermore, the photocatalytic capability of green synthesized rGO/MgO nanocomposite were employed to investigate the Methylene Blue degradation under solar irradiation when exposed to sunlight for 90 min, about 93 % of the dye was degraded. Disc diffusion method was performed to test the antibacterial activity against S. aureus and Escherichia coli at higher concentration of nanocomposite reveals that S. aureus showed maximum zone of inhibition. rGO/MgO material in terms of bioremediation of domestic and industrial waste by killing pathogenic bacteria, breaking down of colourant. The rGO/MgO nanocomposite's promise in environmental remediation is demonstrated by these results, especially in wastewater treatment for dye degradation and harmful bacteria suppression.},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Current study demonstrates the preparation of rGO/MgO nanocomposite using Hylocereus polyrhizus due to its functional benefits, and green strategy method. Scanning electron microscopy (SEM) with Energy Dispersive X-ray analysis and X-ray diffraction were used to find the presence of crystal structure and the elemental analysis. XRD results revealed the cubic structure of the synthesized nanocomposite. The average crystallite size was 36.16 nm. The SEM portrays the agglomerated granular structure has larger scale web like structure are highly interconnected with large voids. The presence of graphene, magnesium and oxygen were analysed from the EDAX elemental studies. The presence of MgO and rGO diffraction planes is well matches with JCPDS card no. 04-0829 and 75-2078. Mg O C stretching vibrations and C O stretching vibrations reflected from FTIR studies confirms the presence of Mg and C in prepared composite. Furthermore, the photocatalytic capability of green synthesized rGO/MgO nanocomposite were employed to investigate the Methylene Blue degradation under solar irradiation when exposed to sunlight for 90 min, about 93 % of the dye was degraded. Disc diffusion method was performed to test the antibacterial activity against S. aureus and Escherichia coli at higher concentration of nanocomposite reveals that S. aureus showed maximum zone of inhibition. rGO/MgO material in terms of bioremediation of domestic and industrial waste by killing pathogenic bacteria, breaking down of colourant. The rGO/MgO nanocomposite's promise in environmental remediation is demonstrated by these results, especially in wastewater treatment for dye degradation and harmful bacteria suppression.
5.
Suhas, U.; Shashidhara, K. N.; Raghavendra, M. J.; Billady, R. K.; Balaji, S.
Optimizing Wear Characteristics of Aluminium Powder Reinforced Epoxy Polymer Matrix Composite Using Taguchi Grey Relational Analysis Approach Journal Article
In: Journal of The Institution of Engineers (India): Series D, vol. 106, pp. 1707-1721,, 2025, ISBN: 22502122 (ISSN), (1).
@article{713,
title = {Optimizing Wear Characteristics of Aluminium Powder Reinforced Epoxy Polymer Matrix Composite Using Taguchi Grey Relational Analysis Approach},
author = {U. Suhas and K. N. Shashidhara and M. J. Raghavendra and R. K. Billady and S. Balaji},
url = {https://link.springer.com/article/10.1007/s40033-024-00768-8},
doi = {10.1007/s40033-024-00768-8},
isbn = {22502122 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of The Institution of Engineers (India): Series D},
volume = {106},
pages = {1707-1721,},
publisher = {Springer},
abstract = {Aluminium-epoxy composites, known for their improved mechanical and tribological properties, exhibit significant potential in various engineering applications. This study investigates the wear behaviour of these composites with a focus on optimization for improved performance. An appropriate manufacturing approach was utilized in order to incorporate aluminum particles into an epoxy matrix as part of the fabrication process. An apparatus that was specifically designed for testing was utilized in order to assess the wear characteristics. For the purpose of designing trials and optimizing the parameters of the wear process, Taguchi's approach was applied. Zircon particles were found to be distributed uniformly throughout the Al-epoxy composites, as demonstrated by the visual evidence obtained from scanning microscopy samples. Statistical analysis revealed that the factor load is the most significant contribution to wear, accounting for roughly 5.67% of the observed wear. Sliding distance, speed, and composite load were determined to be the next most significant contributors to wear. Finding the COF in composites can be accomplished by the use of a prediction tool that is the regression equation that was generated from the data. According to the GRA response table, the best set of parameters for improving the wear properties is as follows: the applied load should be 35 N, the sliding speed should be 250 m/s, and the sliding distance should be approximately 1650 m. A scanning electron microscopy examination of the surface that had been worn revealed that the Al-epoxy composite had exhibited steady wear with the parameters adjusted to their ideal values.},
note = {1},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Aluminium-epoxy composites, known for their improved mechanical and tribological properties, exhibit significant potential in various engineering applications. This study investigates the wear behaviour of these composites with a focus on optimization for improved performance. An appropriate manufacturing approach was utilized in order to incorporate aluminum particles into an epoxy matrix as part of the fabrication process. An apparatus that was specifically designed for testing was utilized in order to assess the wear characteristics. For the purpose of designing trials and optimizing the parameters of the wear process, Taguchi's approach was applied. Zircon particles were found to be distributed uniformly throughout the Al-epoxy composites, as demonstrated by the visual evidence obtained from scanning microscopy samples. Statistical analysis revealed that the factor load is the most significant contribution to wear, accounting for roughly 5.67% of the observed wear. Sliding distance, speed, and composite load were determined to be the next most significant contributors to wear. Finding the COF in composites can be accomplished by the use of a prediction tool that is the regression equation that was generated from the data. According to the GRA response table, the best set of parameters for improving the wear properties is as follows: the applied load should be 35 N, the sliding speed should be 250 m/s, and the sliding distance should be approximately 1650 m. A scanning electron microscopy examination of the surface that had been worn revealed that the Al-epoxy composite had exhibited steady wear with the parameters adjusted to their ideal values.
6.
Kumaraswamy, J.; Bharath, L.; Anil, K. C.; Geetha, T. M.; Nagesh, R.
Results in mechanical properties and wear behaviour of AA6061-Si3N4 composites Journal Article
In: Results in Surfaces and Interfaces, vol. 18, 2025, ISBN: 26668459 (ISSN), (0).
@article{353,
title = {Results in mechanical properties and wear behaviour of AA6061-Si3N4 composites},
author = {J. Kumaraswamy and L. Bharath and K. C. Anil and T. M. Geetha and R. Nagesh},
url = {https://www.sciencedirect.com/science/article/pii/S266684592400196X?via%3Dihub},
doi = {10.1016/j.rsurfi.2024.100376},
isbn = {26668459 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Results in Surfaces and Interfaces},
volume = {18},
publisher = {Elsevier B.V.},
abstract = {The wear attributes of in-situ stir cast AA 6061-Si3N4 nanocomposites were examined in this study. The composites were made using varied Si3N4 concentrations (3.0, 6.0, 9.0, and 12.0 wt%) in a metal salt reaction. Optical microscopy and Energy Dispersive X-ray Spectrometry (EDS) were both used to analyse the microstructure of the composite materials. The homogeneous distribution of Si3N4 particles in the AA 6061 matrix was clearly visible in the microstructures. A dry sliding wear test was conducted using a pin-on-disk tester under various time, load, sliding distance, and speed conditions. The composite materials shown better wear resistance than the AA 6061 matrix. Additionally, the wear rate decreased as Si3N4 content increased for all applied loads, sliding distances, and velocities. Due to the incorporation of tiny silicon nitride particles into the matrix and the strong interfacial connection between the in-situ reinforcement and the matrix alloy, the composites showed decreased wear rates. It was revealed that the wear rate of Si3N4 reinforced composites was comparable to that of automotive industry-used cast iron brake drums. According to SEM images, abrasive wear at lower stresses and adhesive wear at higher loads mostly helped in material removal.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The wear attributes of in-situ stir cast AA 6061-Si3N4 nanocomposites were examined in this study. The composites were made using varied Si3N4 concentrations (3.0, 6.0, 9.0, and 12.0 wt%) in a metal salt reaction. Optical microscopy and Energy Dispersive X-ray Spectrometry (EDS) were both used to analyse the microstructure of the composite materials. The homogeneous distribution of Si3N4 particles in the AA 6061 matrix was clearly visible in the microstructures. A dry sliding wear test was conducted using a pin-on-disk tester under various time, load, sliding distance, and speed conditions. The composite materials shown better wear resistance than the AA 6061 matrix. Additionally, the wear rate decreased as Si3N4 content increased for all applied loads, sliding distances, and velocities. Due to the incorporation of tiny silicon nitride particles into the matrix and the strong interfacial connection between the in-situ reinforcement and the matrix alloy, the composites showed decreased wear rates. It was revealed that the wear rate of Si3N4 reinforced composites was comparable to that of automotive industry-used cast iron brake drums. According to SEM images, abrasive wear at lower stresses and adhesive wear at higher loads mostly helped in material removal.
7.
Omara, A. A. M.; Mohammedali, A. A. M.; Dhivagar, R.
Conical solar stills: A review Journal Article
In: Solar Energy, vol. 288, 2025, ISBN: 0038092X (ISSN), (0).
@article{367,
title = {Conical solar stills: A review},
author = {A. A. M. Omara and A. A. M. Mohammedali and R. Dhivagar},
doi = {10.1016/j.solener.2025.113281},
isbn = {0038092X (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Solar Energy},
volume = {288},
publisher = {Elsevier Ltd},
abstract = {The conical solar still (CSS) is an advanced solar distillation design featuring a cone-shaped glass cover for condensation. This innovative design offers several advantages over conventional solar stills, including reduced shading effects, a larger solar absorption area, uniform solar exposure, and an optimized space-to-productivity ratio. With a demonstrated productivity of 8.09 kg/m2, a production cost of 1.6 $/m3, and a CO2 mitigation of 44.47 tons, the CSS showcases superior energy, economic, and environmental performance compared to alternative designs. This paper provides a comprehensive evaluation of the advancements in CSS research, including various strategies to enhance efficiency. It offers an in-depth analysis of the thermal and design factors influencing CSS performance, encompassing both conventional and advanced configurations. Additionally, the study examines the economic and environmental aspects of CSSs, presenting a comparative analysis against traditional solar still designs. Practical guidance is also provided for the large-scale implementation of CSSs, emphasizing their feasibility and scalability. Finally, the paper identifies the limitations of current CSS technology and explores future opportunities for wider adoption and development. This extensive review serves as a clear roadmap for future designers, enabling the creation of commercial CSS systems that maximize productivity while remaining cost-effective and environmentally sustainable. © 2025 International Solar Energy Society},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The conical solar still (CSS) is an advanced solar distillation design featuring a cone-shaped glass cover for condensation. This innovative design offers several advantages over conventional solar stills, including reduced shading effects, a larger solar absorption area, uniform solar exposure, and an optimized space-to-productivity ratio. With a demonstrated productivity of 8.09 kg/m2, a production cost of 1.6 $/m3, and a CO2 mitigation of 44.47 tons, the CSS showcases superior energy, economic, and environmental performance compared to alternative designs. This paper provides a comprehensive evaluation of the advancements in CSS research, including various strategies to enhance efficiency. It offers an in-depth analysis of the thermal and design factors influencing CSS performance, encompassing both conventional and advanced configurations. Additionally, the study examines the economic and environmental aspects of CSSs, presenting a comparative analysis against traditional solar still designs. Practical guidance is also provided for the large-scale implementation of CSSs, emphasizing their feasibility and scalability. Finally, the paper identifies the limitations of current CSS technology and explores future opportunities for wider adoption and development. This extensive review serves as a clear roadmap for future designers, enabling the creation of commercial CSS systems that maximize productivity while remaining cost-effective and environmentally sustainable. © 2025 International Solar Energy Society
8.
Poyyamozhi, N.; Logesh, K.; Karthikeyan, L.; Vengadesan, E.; Arulprakasajothi, M.
Comparative analysis of salt gradient solar pond energy storage and PCM-coupled TiO2 nanoparticles for enhanced solar energy utilization Journal Article
In: Journal of Thermal Analysis and Calorimetry, 2025, ISBN: 13886150 (ISSN), (0).
@article{380,
title = {Comparative analysis of salt gradient solar pond energy storage and PCM-coupled TiO2 nanoparticles for enhanced solar energy utilization},
author = {N. Poyyamozhi and K. Logesh and L. Karthikeyan and E. Vengadesan and M. Arulprakasajothi},
doi = {10.1007/s10973-024-13964-1},
isbn = {13886150 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Journal of Thermal Analysis and Calorimetry},
publisher = {Springer Science and Business Media B.V.},
abstract = {This study investigates the thermal performance of salt gradient solar ponds with varying compositions of phase change materials (PCMs) and TiO2 nanoparticles. The analysis focused on hourly, daily, weekly, and monthly temperature variations within the lower convective zone (LCZ). Results showed significant improvements in energy storage with the addition of paraffin wax and TiO2. Energy storage capacity increased by 2.3%, 22.2%, and 13.3% for TiO2/paraffin wax compositions of 1%, 2%, and 3% by mass, respectively. Weekly average temperature changes varied from 2 to 5.5 °C, depending on TiO2 concentration. The study highlights the effectiveness of PCM and TiO2 in stabilizing temperature fluctuations, enhancing overall system efficiency. These findings emphasize the potential of salt gradient solar ponds for renewable energy storage. The combination of paraffin wax and TiO2 nanoparticles presents promising opportunities for optimizing energy capture and storage, supporting the transition to cleaner and more sustainable energy solutions.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This study investigates the thermal performance of salt gradient solar ponds with varying compositions of phase change materials (PCMs) and TiO2 nanoparticles. The analysis focused on hourly, daily, weekly, and monthly temperature variations within the lower convective zone (LCZ). Results showed significant improvements in energy storage with the addition of paraffin wax and TiO2. Energy storage capacity increased by 2.3%, 22.2%, and 13.3% for TiO2/paraffin wax compositions of 1%, 2%, and 3% by mass, respectively. Weekly average temperature changes varied from 2 to 5.5 °C, depending on TiO2 concentration. The study highlights the effectiveness of PCM and TiO2 in stabilizing temperature fluctuations, enhancing overall system efficiency. These findings emphasize the potential of salt gradient solar ponds for renewable energy storage. The combination of paraffin wax and TiO2 nanoparticles presents promising opportunities for optimizing energy capture and storage, supporting the transition to cleaner and more sustainable energy solutions.
9.
G, Abhilash; R, Arpitha; J, Raghu; N, Manikanda; Kumar, Mohit
Influence of deep cryogenic and natural ageing treatment on Al 7068- Al2O3 metal matrix composites Journal Article
In: Advances in Materials and Processing Technologies, vol. 11, pp. 368-388,, 2025, ISBN: 2374-068X, (doi: 10.1080/2374068X.2024.2426256).
@article{396,
title = {Influence of deep cryogenic and natural ageing treatment on Al 7068- Al2O3 metal matrix composites},
author = {Abhilash G and Arpitha R and Raghu J and Manikanda N and Mohit Kumar},
url = {https://doi.org/10.1080/2374068X.2024.2426256},
doi = {10.1080/2374068X.2024.2426256},
isbn = {2374-068X},
year = {2025},
date = {2025-01-01},
journal = {Advances in Materials and Processing Technologies},
volume = {11},
pages = {368-388,},
publisher = {Taylor & Francis},
abstract = {Aluminium matrix composites are the leading materials in aerospace and automotive applications due to their excellent structural properties. Cryogenic and ageing are the heat treatment processes that are employed to improve the performance of aluminium alloy components. The present study details an experimental investigation on deep cryogenic treatment (DCT) and ageing heat treatment (AHT) of Al 7068 reinforced with Al2O3 under various weight propositions. The composites are developed using the stir-casting technique. The proportion of base and reinforcement materials is designed based on the percentage of reinforcement materials, such as 0%, 2%, 4%, 6%, and 8%. The DCT has been conducted at 77 K, and ageing has been conducted at 688 K (for 2 h) and 399 K (24?hours), followed by water quenching. The heat-treated samples were subjected to the tensile test, compression test, hardness test, and wear analysis. In addition to that, fractural and surface wear analysis is also carried out through scanning electron microscopy (SEM). The final results show that Al7068 with 6% Al2O3 reinforcement has a greater impact than other matrix combinations.},
note = {doi: 10.1080/2374068X.2024.2426256},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Aluminium matrix composites are the leading materials in aerospace and automotive applications due to their excellent structural properties. Cryogenic and ageing are the heat treatment processes that are employed to improve the performance of aluminium alloy components. The present study details an experimental investigation on deep cryogenic treatment (DCT) and ageing heat treatment (AHT) of Al 7068 reinforced with Al2O3 under various weight propositions. The composites are developed using the stir-casting technique. The proportion of base and reinforcement materials is designed based on the percentage of reinforcement materials, such as 0%, 2%, 4%, 6%, and 8%. The DCT has been conducted at 77 K, and ageing has been conducted at 688 K (for 2 h) and 399 K (24?hours), followed by water quenching. The heat-treated samples were subjected to the tensile test, compression test, hardness test, and wear analysis. In addition to that, fractural and surface wear analysis is also carried out through scanning electron microscopy (SEM). The final results show that Al7068 with 6% Al2O3 reinforcement has a greater impact than other matrix combinations.
10.
Sivaramkrishnan, M.; Ramkumar, M. S.; Arun, M.; Kanan, M.; Shankar, S.; Giri, J.
Enhanced Wastewater Treatment Plant Feature Prediction Using Edge Attention Network with Parrot Optimization Proceedings
Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152392-3 (ISBN), (0).
@proceedings{412,
title = {Enhanced Wastewater Treatment Plant Feature Prediction Using Edge Attention Network with Parrot Optimization},
author = {M. Sivaramkrishnan and M. S. Ramkumar and M. Arun and M. Kanan and S. Shankar and J. Giri},
url = {https://ieeexplore.ieee.org/document/10933019},
doi = {10.1109/ICSADL65848.2025.10933019},
isbn = {979-833152392-3 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings},
pages = {1585-1591,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {To assist process design and controls, enhance system dependability, lower operating costs, and support overall performance optimization, an accurate prediction of wastewater treatment plant (WWTP) key features can help understand and predict plant behavior. In order to address the non-linearity and dynamic character of environmental data, deep learning technologies, which have been shown to be data-driven soft sensors, should be developed for WWTP applications. In order to predict important WWTP parameters such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption, this work uses deep learning-based models as soft sensors. The proposed method predicts key features of wastewater treatment plants (WWTPs) by using a Multilayer Edge Attention Network with Parrot Optimizer (MEA-Net-PO) and a Hybrid Osprey Optimization Algorithm with Emperor Penguin Optimizer-Based Feature Selection (OOA-EPO). The approach adopts MinMax scaler normalization for preprocessing data to obtain optimal performance in high-dimensional datasets. The MEA-Net-PO model encompasses complex relationships among WWTP data, while OOA-EPO feature selection promotes the selection of the most suitable input features. This does not rely on data distribution assumptions but is very elastic for a given WWTP. The model improves the forecasting efficiency and accuracy. Using historical data from a municipal WWTP, located along a coastal area of Saudi Arabia, the model clearly outperforms traditional methods through RMSE, MAPE, and R2. This may be considered the most promising avenue for data-driven optimization in managing WWTP operations.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
To assist process design and controls, enhance system dependability, lower operating costs, and support overall performance optimization, an accurate prediction of wastewater treatment plant (WWTP) key features can help understand and predict plant behavior. In order to address the non-linearity and dynamic character of environmental data, deep learning technologies, which have been shown to be data-driven soft sensors, should be developed for WWTP applications. In order to predict important WWTP parameters such as influent flow, influent temperature, influent biochemical oxygen demand (BOD), effluent chloride, effluent BOD, and power consumption, this work uses deep learning-based models as soft sensors. The proposed method predicts key features of wastewater treatment plants (WWTPs) by using a Multilayer Edge Attention Network with Parrot Optimizer (MEA-Net-PO) and a Hybrid Osprey Optimization Algorithm with Emperor Penguin Optimizer-Based Feature Selection (OOA-EPO). The approach adopts MinMax scaler normalization for preprocessing data to obtain optimal performance in high-dimensional datasets. The MEA-Net-PO model encompasses complex relationships among WWTP data, while OOA-EPO feature selection promotes the selection of the most suitable input features. This does not rely on data distribution assumptions but is very elastic for a given WWTP. The model improves the forecasting efficiency and accuracy. Using historical data from a municipal WWTP, located along a coastal area of Saudi Arabia, the model clearly outperforms traditional methods through RMSE, MAPE, and R2. This may be considered the most promising avenue for data-driven optimization in managing WWTP operations.
11.
Ramkumar, M. S.; Sivaramkrishnan, M.; Kannaiyan, M.; Kanan, M.; Shankar, S.; Giri, J.
Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833150574-5 (ISBN), (0).
@proceedings{433,
title = {Micro-Grid Fault Detection and Classification for Smart Vehicles Using Simple and Efficient Metapath Aggregated Network-Sea Horse Optimization Approach},
author = {M. S. Ramkumar and M. Sivaramkrishnan and M. Kannaiyan and M. Kanan and S. Shankar and J. Giri},
url = {https://ieeexplore.ieee.org/document/10967681},
doi = {10.1109/ICMLAS64557.2025.10967681},
isbn = {979-833150574-5 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025 - Proceedings},
pages = {1254-1259,},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Microgrid (MG) Fault Detection (FD) and classification are crucial for maintaining stability and reliability, especially with Smart Vehicle (SV) integration. Effective FD and classification helps identify and mitigate faults quickly, preventing power disruptions and ensuring smooth operation of the system. The system struggles with bi-directional power flow since identifying normal fluctuations from faults becomes a problem resulting in incorrect assessments or delayed operations. To overcome these drawbacks, this paper proposes an improving MG FD and Classification for SV using Simple and Efficient Metapath Aggregated Network (SEMAN) and Sea Horse Optimizer (SHO) approach. The process begins by gathering data from operational MG infrastructure dataset, which is then passed through a preprocessing phase. Fairness-Aware collaborative filtering (FACF) are employed to clean and remove the missing value in the input data. Once pre-processed, the data enters the prediction and classification phase, to enhance the accuracy of predictions. The phase-to-ground, phase-to-phase, phase-phase-to-ground and three phase faults are successfully predicted and classified by using SEMAN. The weight parameter of SEMAN is optimized using SHO. The SEMAN-SHO technique is implemented in MATLAB and evaluated using various performance metrics, including accuracy, precision, recall, and Root Mean Squared Error (RMSE). The results demonstrates that the SEMAN-SHO method performs better than the existing approaches, such as Zebra Optimization Algorithm with Spiking Neural Network (ZOA-SNN), Long Short-Term Memory- Adaptive Neuro Fuzzy Inference System (LSTM-ANFIS), Machine Learning (ML), Particle Swarm Optimization Back Propagation (PSO-BP) and Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM). These results demonstrate the effectiveness of the proposed method in accurately detecting and classifying faults in MG for SV, with a high accuracy of 98.5%, precision of 98.4%, recall of 98.9%, and an RMSE of 0.62, ensuring reliable and stable system operation.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Microgrid (MG) Fault Detection (FD) and classification are crucial for maintaining stability and reliability, especially with Smart Vehicle (SV) integration. Effective FD and classification helps identify and mitigate faults quickly, preventing power disruptions and ensuring smooth operation of the system. The system struggles with bi-directional power flow since identifying normal fluctuations from faults becomes a problem resulting in incorrect assessments or delayed operations. To overcome these drawbacks, this paper proposes an improving MG FD and Classification for SV using Simple and Efficient Metapath Aggregated Network (SEMAN) and Sea Horse Optimizer (SHO) approach. The process begins by gathering data from operational MG infrastructure dataset, which is then passed through a preprocessing phase. Fairness-Aware collaborative filtering (FACF) are employed to clean and remove the missing value in the input data. Once pre-processed, the data enters the prediction and classification phase, to enhance the accuracy of predictions. The phase-to-ground, phase-to-phase, phase-phase-to-ground and three phase faults are successfully predicted and classified by using SEMAN. The weight parameter of SEMAN is optimized using SHO. The SEMAN-SHO technique is implemented in MATLAB and evaluated using various performance metrics, including accuracy, precision, recall, and Root Mean Squared Error (RMSE). The results demonstrates that the SEMAN-SHO method performs better than the existing approaches, such as Zebra Optimization Algorithm with Spiking Neural Network (ZOA-SNN), Long Short-Term Memory- Adaptive Neuro Fuzzy Inference System (LSTM-ANFIS), Machine Learning (ML), Particle Swarm Optimization Back Propagation (PSO-BP) and Convolutional Neural Network- Long Short-Term Memory (CNN-LSTM). These results demonstrate the effectiveness of the proposed method in accurately detecting and classifying faults in MG for SV, with a high accuracy of 98.5%, precision of 98.4%, recall of 98.9%, and an RMSE of 0.62, ensuring reliable and stable system operation.
12.
Arulprakasajothi, M.; Karthikeyan, L.; Saranya, A.; Poyyamozhi, N.; Prabhakar, P.
In: Thermal Science and Engineering Progress, vol. 63, 2025, ISBN: 24519049 (ISSN), (0).
@article{442,
title = {Comparative analysis of air-based Photovoltaic Thermal (PVT) systems with enhanced performance through fins and baffles: Experimental and CFD study},
author = {M. Arulprakasajothi and L. Karthikeyan and A. Saranya and N. Poyyamozhi and P. Prabhakar},
url = {https://www.sciencedirect.com/science/article/pii/S2451904925005268?via%3Dihub},
doi = {10.1016/j.tsep.2025.103736},
isbn = {24519049 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Thermal Science and Engineering Progress},
volume = {63},
publisher = {Elsevier Ltd},
abstract = {This paper presents a study focusing on the performance evaluation of an air-based Photovoltaic Thermal (PVT) system equipped with fins and baffles, denoted as Model 1, alongside Model 2 and Model 3. The investigation aims to understand how a single mass flow rate can lead to variations in performance, pressure drop, irreversibility, exit temperature, maximum surface temperature, and heat loss among these different models. Experiments were conducted with three models under a constant volume flow rate of 0.00847 kg/s, and the findings were verified using Computational Fluid Dynamics (CFD) simulations. During the experimental phase, all three models exhibited efficiency levels ranging from 22.79 % to 44.70 %. The exergy effectiveness across the CFD-simulated scenarios for these models spanned from 24.32 % to 45.40 %. Model 1 showed the highest irreversibility, measuring 354 W, while Model 3 displayed the lowest at 269 W. Model 3 achieved the highest performance efficiency, reaching 45.40 %, whereas Model 1 yielded the lowest performance, registering at 22.79 %. Furthermore, a substantial performance improvement of 38 % and 56 % was observed when transitioning from Model 2 to Model 3.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This paper presents a study focusing on the performance evaluation of an air-based Photovoltaic Thermal (PVT) system equipped with fins and baffles, denoted as Model 1, alongside Model 2 and Model 3. The investigation aims to understand how a single mass flow rate can lead to variations in performance, pressure drop, irreversibility, exit temperature, maximum surface temperature, and heat loss among these different models. Experiments were conducted with three models under a constant volume flow rate of 0.00847 kg/s, and the findings were verified using Computational Fluid Dynamics (CFD) simulations. During the experimental phase, all three models exhibited efficiency levels ranging from 22.79 % to 44.70 %. The exergy effectiveness across the CFD-simulated scenarios for these models spanned from 24.32 % to 45.40 %. Model 1 showed the highest irreversibility, measuring 354 W, while Model 3 displayed the lowest at 269 W. Model 3 achieved the highest performance efficiency, reaching 45.40 %, whereas Model 1 yielded the lowest performance, registering at 22.79 %. Furthermore, a substantial performance improvement of 38 % and 56 % was observed when transitioning from Model 2 to Model 3.
13.
Bharath, L.; Kumaraswamy, J.; Purohit, R.; Suresh, R.
In: Next Materials, vol. 8, 2025, ISBN: 29498228 (ISSN), (0).
@article{459,
title = {Role of hot-rolling and heat-treatment of in-situ chemical reaction of Al7075/Gr./TiB2 hybrid composite in microstructure, grain size and yield strength},
author = {L. Bharath and J. Kumaraswamy and R. Purohit and R. Suresh},
url = {https://www.sciencedirect.com/science/article/pii/S2949822825004150?via%3Dihub},
doi = {10.1016/j.nxmate.2025.100897},
isbn = {29498228 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Next Materials},
volume = {8},
publisher = {Elsevier B.V.},
abstract = {This paper discovers the effect of hot rolling and heat-treatment on Al7075/Gr./TiB2 hybrid composite fabricated through in-situ chemical reaction process. The prepared cast specimens have been machined to a size of 45 × 50 × 11 mm and specimen was heated to 400℃ for 1 hr. duration in an electric furnace. Further, heated specimen has been hot-rolled and 80 % reduction was achieved. Thereafter, microstructure test samples and tensile test samples were machined as per ASTM-E3 and ASTM-A370 standard test size. It is observed that, on polished surface higher weight percentage of graphite particles, cluster formation and uniform dispersion are noticed before rolling. However, after hot-rolling the clustering was scarcely seen which is related to strain-rate induced during hot-rolling process, size of graphite particle and its weight percentage. Grain size for hot-rolled Al7075/8 % Gr./5 %TiB2 exhibits reduction of 36 % when assisted with Al7075 alloy alone. However, yield strength exhibits improvement of 58 %.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This paper discovers the effect of hot rolling and heat-treatment on Al7075/Gr./TiB2 hybrid composite fabricated through in-situ chemical reaction process. The prepared cast specimens have been machined to a size of 45 × 50 × 11 mm and specimen was heated to 400℃ for 1 hr. duration in an electric furnace. Further, heated specimen has been hot-rolled and 80 % reduction was achieved. Thereafter, microstructure test samples and tensile test samples were machined as per ASTM-E3 and ASTM-A370 standard test size. It is observed that, on polished surface higher weight percentage of graphite particles, cluster formation and uniform dispersion are noticed before rolling. However, after hot-rolling the clustering was scarcely seen which is related to strain-rate induced during hot-rolling process, size of graphite particle and its weight percentage. Grain size for hot-rolled Al7075/8 % Gr./5 %TiB2 exhibits reduction of 36 % when assisted with Al7075 alloy alone. However, yield strength exhibits improvement of 58 %.
14.
Alagumalai, V.; Rajamanickam, V.; Mohamed, H. G.; Badruddin, I. A.; Bashir, M. N.; Mensah, R. A.
In: Scientific Reports, vol. 15, 2025, ISBN: 20452322 (ISSN), (0).
@article{472,
title = {Optimisation of areca nut husk-derived cellulose nanofibers for enhancing the mechanical properties of epoxy composites using response surface methodology},
author = {V. Alagumalai and V. Rajamanickam and H. G. Mohamed and I. A. Badruddin and M. N. Bashir and R. A. Mensah},
url = {https://www.nature.com/articles/s41598-025-11415-x},
doi = {10.1038/s41598-025-11415-x},
isbn = {20452322 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Scientific Reports},
volume = {15},
publisher = {Nature Research},
abstract = {Areca nut husk, a widely available yet underutilised agro-waste, is explored in this study as a novel and sustainable source of cellulose nanofibers (CNFs), addressing both environmental concerns and the growing demand for bio-based reinforcements in polymer composites. CNFs were extracted using alkali and bleaching treatments followed by ultrasonication, yielding fibres with a mean diameter of 35.84 nm. Epoxy composites were fabricated with CNF loadings ranging from 0.1 to 1.0 wt%, while key processing parameters, including mixing time (10–30 min) and curing temperature (60–100 °C), were optimised using Response Surface Methodology (RSM) based on a Box–Behnken Design. The developed regression models exhibited high predictive accuracy, with R2 values of 99.63% for tensile strength and 99.80% for flexural strength. Analysis of variance (ANOVA) identified CNF content as the most influential factor (F = 934.48 and 1646.71 for tensile and flexural strength, respectively), followed by mixing time and curing temperature. Optimised conditions of approximately 1.5 wt% CNF, 22 min mixing, and 80 °C curing yielded experimentally validated tensile and flexural strengths of 61.88 MPa and 74.36 MPa, respectively, deviating by only 5.27% and 2.76% from model predictions. These results confirm the effectiveness of process-optimised CNF incorporation in enhancing mechanical performance and highlight the potential of areca nut husk as a viable, high-performance bio-reinforcement for next-generation green composites.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Areca nut husk, a widely available yet underutilised agro-waste, is explored in this study as a novel and sustainable source of cellulose nanofibers (CNFs), addressing both environmental concerns and the growing demand for bio-based reinforcements in polymer composites. CNFs were extracted using alkali and bleaching treatments followed by ultrasonication, yielding fibres with a mean diameter of 35.84 nm. Epoxy composites were fabricated with CNF loadings ranging from 0.1 to 1.0 wt%, while key processing parameters, including mixing time (10–30 min) and curing temperature (60–100 °C), were optimised using Response Surface Methodology (RSM) based on a Box–Behnken Design. The developed regression models exhibited high predictive accuracy, with R2 values of 99.63% for tensile strength and 99.80% for flexural strength. Analysis of variance (ANOVA) identified CNF content as the most influential factor (F = 934.48 and 1646.71 for tensile and flexural strength, respectively), followed by mixing time and curing temperature. Optimised conditions of approximately 1.5 wt% CNF, 22 min mixing, and 80 °C curing yielded experimentally validated tensile and flexural strengths of 61.88 MPa and 74.36 MPa, respectively, deviating by only 5.27% and 2.76% from model predictions. These results confirm the effectiveness of process-optimised CNF incorporation in enhancing mechanical performance and highlight the potential of areca nut husk as a viable, high-performance bio-reinforcement for next-generation green composites.
15.
Basavaraj, K.; Kulkarni, A.
Optimization of process parameters in explosive welding using machine learning Proceedings
Association of American Publishers, vol. 55, 2025, ISBN: 24743941 (ISSN); 978-164490360-5 (ISBN), (0).
@proceedings{473,
title = {Optimization of process parameters in explosive welding using machine learning},
author = {K. Basavaraj and A. Kulkarni},
url = {https://mrforum.com/product/9781644903612-9/},
doi = {10.21741/9781644903612-9},
isbn = {24743941 (ISSN); 978-164490360-5 (ISBN)},
year = {2025},
date = {2025-01-01},
journal = {Materials Research Proceedings},
volume = {55},
pages = {51-56,},
publisher = {Association of American Publishers},
abstract = {A solid-state welding technique that joins two pieces of metal by controlled explosive detonation is called explosive welding (EXW), which has become a promising area of the study. However, it is well known that explosive welding is an expensive experiment. It is tough to expect the experimental results based on a practical approach by repeated attempts which are continued until success. In the present paper, though several Artificial Intelligence (AI) algorithms are implemented and trained using the dataset, the current state of AI algorithms based on the previous studies and their findings applied to the optimization of the welding process is reviewed and explained. Also, the types of optimization techniques available in order to predict the best results and most relevant input factors of explosive welding are reviewed. Based on the survey, the best optimisation technique is suggested for researchers.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
A solid-state welding technique that joins two pieces of metal by controlled explosive detonation is called explosive welding (EXW), which has become a promising area of the study. However, it is well known that explosive welding is an expensive experiment. It is tough to expect the experimental results based on a practical approach by repeated attempts which are continued until success. In the present paper, though several Artificial Intelligence (AI) algorithms are implemented and trained using the dataset, the current state of AI algorithms based on the previous studies and their findings applied to the optimization of the welding process is reviewed and explained. Also, the types of optimization techniques available in order to predict the best results and most relevant input factors of explosive welding are reviewed. Based on the survey, the best optimisation technique is suggested for researchers.
16.
Siddeshkumar, N; Manjula, H; Balakrishnan, K; Pramod, T; Shankar, B.; Khan, Arshan; Pruthvi, H; K, Santosh; Nandakumar, M; Prabhu, Subramanya; C, Durga
Study on dry sliding wear behavior and machine learning models for wear rate prediction of nano hybrid 2219 MMCs reinforced with n-B4C & MoS2 Journal Article
In: vol. 20, 2025.
@article{617,
title = {Study on dry sliding wear behavior and machine learning models for wear rate prediction of nano hybrid 2219 MMCs reinforced with n-B4C & MoS2},
author = {N Siddeshkumar and H Manjula and K Balakrishnan and T Pramod and B. Shankar and Arshan Khan and H Pruthvi and Santosh K and M Nandakumar and Subramanya Prabhu and Durga C},
url = {https://www.sciencedirect.com/science/article/pii/S2666845925002107?via%3Dihub},
doi = {10.1016/j.rsurfi.2025.100623},
year = {2025},
date = {2025-01-01},
volume = {20},
abstract = {This study examines the dry sliding wear behaviour of stir-cast Aluminium 2219 nano composites reinforced with nano boron carbide and a hybrid combination of nano boron carbide & Molybdenum disulfide particles. The wear properties were analyzed under varying conditions of sliding speed (0.65-13.0 m per second), applied load (5-100 N), and sliding distance (250-5000 m). Key factors such as nanoparticle dispersion, density, hardness, and wear resistance were investigated. The results reveal that adding nanoparticles enhances wear resistance, which correlates with increased hardness. Both Aluminium 2219 composites reinforced with nano boron carbide and hybrid (nano boron carbide & Molybdenum disulfide) reinforcements exhibit similar wear behaviour trends. However, the hybrid composites (nano boron carbide & Molybdenum disulfide) demonstrate significantly improved wear resistance across all tested conditions, with MoS 2 inclusion playing a crucial role in further enhancing wear resistance. The particular wear rate of Aluminium 2219 was predicted using machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, K-Nearest Neighbors, Option Tree, Random Forest, and Gradient Boosting. The primary input parameters, applied load, sliding speed, and sliding distance, were analyzed to determine their impact on wear rate. Several criteria were used to assess the performance of various machine learning models. Ensemble models such as Random Forest, Decision Tree, and Gradient Boosting, along with K-Nearest Neighbors, exhibited minimal deviations and demonstrated robust predictive accuracy.},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This study examines the dry sliding wear behaviour of stir-cast Aluminium 2219 nano composites reinforced with nano boron carbide and a hybrid combination of nano boron carbide & Molybdenum disulfide particles. The wear properties were analyzed under varying conditions of sliding speed (0.65-13.0 m per second), applied load (5-100 N), and sliding distance (250-5000 m). Key factors such as nanoparticle dispersion, density, hardness, and wear resistance were investigated. The results reveal that adding nanoparticles enhances wear resistance, which correlates with increased hardness. Both Aluminium 2219 composites reinforced with nano boron carbide and hybrid (nano boron carbide & Molybdenum disulfide) reinforcements exhibit similar wear behaviour trends. However, the hybrid composites (nano boron carbide & Molybdenum disulfide) demonstrate significantly improved wear resistance across all tested conditions, with MoS 2 inclusion playing a crucial role in further enhancing wear resistance. The particular wear rate of Aluminium 2219 was predicted using machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, K-Nearest Neighbors, Option Tree, Random Forest, and Gradient Boosting. The primary input parameters, applied load, sliding speed, and sliding distance, were analyzed to determine their impact on wear rate. Several criteria were used to assess the performance of various machine learning models. Ensemble models such as Random Forest, Decision Tree, and Gradient Boosting, along with K-Nearest Neighbors, exhibited minimal deviations and demonstrated robust predictive accuracy.
17.
Rajamani, Rejin; Vetrithangam, D.; Bhor, Harsh; Verma, R.
Secure IoT-Integrated Cloud-Based Medical Image Processing Using Optimized Stereoscopic Scalable Quantum CNN for Efficient Diagnosis Journal Article
In: International Journal of Image and Graphics, pp. 2750066+, 2025, ISBN: 0219-4678.
@article{619,
title = {Secure IoT-Integrated Cloud-Based Medical Image Processing Using Optimized Stereoscopic Scalable Quantum CNN for Efficient Diagnosis},
author = {Rejin Rajamani and D. Vetrithangam and Harsh Bhor and R. Verma},
url = {https://www.worldscientific.com/doi/10.1142/S0219467827500665},
doi = {10.1142/S0219467827500665},
isbn = {0219-4678},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Image and Graphics},
pages = {2750066+},
publisher = {World Scientific Publishing Co.},
abstract = {Healthcare data have increased significantly as a result of the quick development of medical imaging technology, necessitating accurate diagnosis, safe transmission, and effective storage. Using a hybrid model known as stereoscopic scalable quantum convolutional neural network-Gooseneck Barnacle optimization (SSQ-CNN-GBO), this study suggests an innovative, safe, and scalable cloud-based medical image analysis framework that is integrated with the Internet of Things (IoT). The system uses quasi-cross bilateral filtering (QBF) for feature preservation and noise reduction, as well as dual elliptic curve-based lightweight authentication and data encryption (DEC-LADE) to guarantee data security. The GBO algorithm is utilized to optimize anSSQ-CNN for classification, while the multiview fuzzy clustering based on anchor graph (MVFCAG) approach is employed for accurate segmentation. Tests on the brain tumor MRI and chestX-ray14 datasets show that the suggested model outperforms current techniques in terms of diagnosis and encryption efficiency, achieving 99.97% accuracy and 98.57% precision. IoT-enabled healthcare systems can process medical images securely, accurately, and in real time thanks to this integrated solution.},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Healthcare data have increased significantly as a result of the quick development of medical imaging technology, necessitating accurate diagnosis, safe transmission, and effective storage. Using a hybrid model known as stereoscopic scalable quantum convolutional neural network-Gooseneck Barnacle optimization (SSQ-CNN-GBO), this study suggests an innovative, safe, and scalable cloud-based medical image analysis framework that is integrated with the Internet of Things (IoT). The system uses quasi-cross bilateral filtering (QBF) for feature preservation and noise reduction, as well as dual elliptic curve-based lightweight authentication and data encryption (DEC-LADE) to guarantee data security. The GBO algorithm is utilized to optimize anSSQ-CNN for classification, while the multiview fuzzy clustering based on anchor graph (MVFCAG) approach is employed for accurate segmentation. Tests on the brain tumor MRI and chestX-ray14 datasets show that the suggested model outperforms current techniques in terms of diagnosis and encryption efficiency, achieving 99.97% accuracy and 98.57% precision. IoT-enabled healthcare systems can process medical images securely, accurately, and in real time thanks to this integrated solution.
18.
Rajendran, Sundarakannan; Palani, Geetha; Sanjeevi, Shankar; Veerasimman, Arumugaprabu; Trilaksana, Herri; Sundaram, Vickram; Marimuthu, Uthayakumar; Yang, Yo-Lun; Shanmugam, Vigneshwaran
A review on AI integration with FDM printing to enhance precision, efficiency, and process optimization Journal Article
In: Journal of Reinforced Plastics and Composites, pp. 07316844251358587+, 2025, ISBN: 0731-6844.
@article{621,
title = {A review on AI integration with FDM printing to enhance precision, efficiency, and process optimization},
author = {Sundarakannan Rajendran and Geetha Palani and Shankar Sanjeevi and Arumugaprabu Veerasimman and Herri Trilaksana and Vickram Sundaram and Uthayakumar Marimuthu and Yo-Lun Yang and Vigneshwaran Shanmugam},
url = {https://journals.sagepub.com/doi/10.1177/07316844251358587},
doi = {10.1177/07316844251358587},
isbn = {0731-6844},
year = {2025},
date = {2025-01-01},
journal = {Journal of Reinforced Plastics and Composites},
pages = {07316844251358587+},
publisher = {SAGE Publications Ltd STM},
abstract = {Fused deposition modeling (FDM) is widely applied in industries such as automotive, aerospace, and healthcare; however, it is limited by print quality, material consumption, and process efficiency. Artificial intelligence (AI) is a game-changing technology that is intended to overcome such limitations. In this review, the use of AI in FDM 3D printing, with special application in real-time error detection, material optimization, predictive maintenance, and generative design, is discussed in detail. AI allows real-time monitoring of the printing process, which leads to dynamic adjustments that improve reliability, minimize material wastage, and enhance structural strength. Efforts have been made on this review in addressing the capability of AI-based solutions to minimize downtime, print setting optimization, and enable mass production of complex, customized parts. Furthermore, the potential of fully autonomous AI-integrated FDM systems in the foreseeable future is discussed. This integration is a significant leap towards the development of FDM efficiency, reliability, and flexibility for industrial applications.},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Fused deposition modeling (FDM) is widely applied in industries such as automotive, aerospace, and healthcare; however, it is limited by print quality, material consumption, and process efficiency. Artificial intelligence (AI) is a game-changing technology that is intended to overcome such limitations. In this review, the use of AI in FDM 3D printing, with special application in real-time error detection, material optimization, predictive maintenance, and generative design, is discussed in detail. AI allows real-time monitoring of the printing process, which leads to dynamic adjustments that improve reliability, minimize material wastage, and enhance structural strength. Efforts have been made on this review in addressing the capability of AI-based solutions to minimize downtime, print setting optimization, and enable mass production of complex, customized parts. Furthermore, the potential of fully autonomous AI-integrated FDM systems in the foreseeable future is discussed. This integration is a significant leap towards the development of FDM efficiency, reliability, and flexibility for industrial applications.
19.
Veeraraghavan, S. M.; Kaliyaperumal, G.; Sampath, S.; Jayabal, R.; Mukilarasan, M.; Manickaraj, P.; Poures, M. V. De; Dillikannan, D.
In: Process Safety and Environmental Protection, vol. 202, 2025, ISBN: 09575820 (ISSN); 17443598 (ISSN), (0).
@article{628,
title = {Optimized injection strategy for hydrogen-waste transformer oil biodiesel dual-fuel engines: A novel waste-to-energy solution towards environmental protection},
author = {S. M. Veeraraghavan and G. Kaliyaperumal and S. Sampath and R. Jayabal and M. Mukilarasan and P. Manickaraj and M. V. De Poures and D. Dillikannan},
url = {https://www.sciencedirect.com/science/article/abs/pii/S095758202501016X?via%3Dihub},
doi = {10.1016/j.psep.2025.107749},
isbn = {09575820 (ISSN); 17443598 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Process Safety and Environmental Protection},
volume = {202},
publisher = {Institution of Chemical Engineers},
abstract = {Increasing concerns about fossil fuel exhaustion, crude price oscillations, scarcity in petroleum resources, and increased environmental concerns have prompted the attention of researchers towards alternatives for petroleum-based fuels. In this study fuel WTOB50, which is 50 % by volume of waste transformer oil biodiesel was utilized in diesel engines with hydrogen in bi-fuel mode. The experimental investigation was conducted with H<inf>2</inf> induction at 8 lpm, WTOB50 as the primary fuel, fuel injection pressures of 170, 210, and 240 bar, and fuel injection timings of 23°bTDC, 21°bTDC, and 19°bTDC, under full load conditions. Findings resulted increased thermal efficiency at a fuel injection pressure (IP) of 240 bar and an injection timing (IT) of 19°bTDC, showing a 4.08 % improvement compared to standard WTOB50 operation. BSEC was reduced by 5.23 % under the same conditions. NO<inf>x</inf> emissions were minimized at an IT of 23°bTDC, while opacity reduced at 19°bTDC with higher injection pressures. Cylinder peak pressure and heat release rate were highest at 19°bTDC, highlighting improved combustion characteristics. Additionally, dual-fuel operation exhibited delayed in ignition periods against diesel, but retarding the FIT to 19°bTDC shortened the ignition delay. Overall, bi-fuel operation with WTOB50 and hydrogen at optimized injection parameters demonstrated notable improvements in engine metrics, indicating the potential of this approach as an effective waste-to-energy solution.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Increasing concerns about fossil fuel exhaustion, crude price oscillations, scarcity in petroleum resources, and increased environmental concerns have prompted the attention of researchers towards alternatives for petroleum-based fuels. In this study fuel WTOB50, which is 50 % by volume of waste transformer oil biodiesel was utilized in diesel engines with hydrogen in bi-fuel mode. The experimental investigation was conducted with H<inf>2</inf> induction at 8 lpm, WTOB50 as the primary fuel, fuel injection pressures of 170, 210, and 240 bar, and fuel injection timings of 23°bTDC, 21°bTDC, and 19°bTDC, under full load conditions. Findings resulted increased thermal efficiency at a fuel injection pressure (IP) of 240 bar and an injection timing (IT) of 19°bTDC, showing a 4.08 % improvement compared to standard WTOB50 operation. BSEC was reduced by 5.23 % under the same conditions. NO<inf>x</inf> emissions were minimized at an IT of 23°bTDC, while opacity reduced at 19°bTDC with higher injection pressures. Cylinder peak pressure and heat release rate were highest at 19°bTDC, highlighting improved combustion characteristics. Additionally, dual-fuel operation exhibited delayed in ignition periods against diesel, but retarding the FIT to 19°bTDC shortened the ignition delay. Overall, bi-fuel operation with WTOB50 and hydrogen at optimized injection parameters demonstrated notable improvements in engine metrics, indicating the potential of this approach as an effective waste-to-energy solution.
20.
Selvan, C.; Kumari, Santha; Shanmugathai, M.; Shankar, S.
Simplicial reflection graph equivariant secretary bird quantum attention networks model for evaluating teaching quality in higher education Journal Article
In: Social Network Analysis and Mining, vol. 15, 2025, ISBN: 18695469 (ISSN); 18695450 (ISSN), (0).
@article{670,
title = {Simplicial reflection graph equivariant secretary bird quantum attention networks model for evaluating teaching quality in higher education},
author = {C. Selvan and Santha Kumari and M. Shanmugathai and S. Shankar},
url = {https://link.springer.com/article/10.1007/s13278-025-01503-1},
doi = {10.1007/s13278-025-01503-1},
isbn = {18695469 (ISSN); 18695450 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Social Network Analysis and Mining},
volume = {15},
publisher = {Springer},
abstract = {Evaluating teaching quality in higher education is vital to ensuring academic excellence and student learning outcomes. But the varied, subjective, and frequently variable nature of assessment indicators makes it a challenge to measure them effectively. In order to address these issues, this research proposes a novel model for evaluating the quality of instruction in higher education using simplicial reflection graph equivariant secretary bird quantum attention networks (SRGE-SBQAN). "Teaching Quality Evaluation in Higher Education," a dataset of 100 records that cover a wide range of teaching attributes from Indian universities, is used to train and evaluate the model. The data preprocessing phase utilizes Correlation Coefficients and Min–Max Normalization to normalize feature scales and minimize noise. Feature extraction is conducted via a Maximum-Entropy Regularized Decision Transformer, which detects the most impactful teaching characteristics. Reflection-Equivariant Quantum Neural Networks (REQNN) and Simplicial Graph Attention Transformers (SGAT) are used in the SRGE-SBQAN model to produce dependable predictions. The Secretary Bird Optimization Algorithm (SBOA) is used to adjust the model's parameters. The suggested model performs exceptionally well in terms of accuracy (99.9%), precision (99.5%), recall (99.7%), specificity (99.2%), F1-score (99.1%), Mean Absolute Error (MAE) (3.7), and Root Mean Square Error (RMSE) (3.3). According to these findings, the model is a powerful instrument for data-driven analysis and quality enhancement in higher education because of its strong generalization capacity, scalability to huge data, and superior interpretability.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Evaluating teaching quality in higher education is vital to ensuring academic excellence and student learning outcomes. But the varied, subjective, and frequently variable nature of assessment indicators makes it a challenge to measure them effectively. In order to address these issues, this research proposes a novel model for evaluating the quality of instruction in higher education using simplicial reflection graph equivariant secretary bird quantum attention networks (SRGE-SBQAN). "Teaching Quality Evaluation in Higher Education," a dataset of 100 records that cover a wide range of teaching attributes from Indian universities, is used to train and evaluate the model. The data preprocessing phase utilizes Correlation Coefficients and Min–Max Normalization to normalize feature scales and minimize noise. Feature extraction is conducted via a Maximum-Entropy Regularized Decision Transformer, which detects the most impactful teaching characteristics. Reflection-Equivariant Quantum Neural Networks (REQNN) and Simplicial Graph Attention Transformers (SGAT) are used in the SRGE-SBQAN model to produce dependable predictions. The Secretary Bird Optimization Algorithm (SBOA) is used to adjust the model's parameters. The suggested model performs exceptionally well in terms of accuracy (99.9%), precision (99.5%), recall (99.7%), specificity (99.2%), F1-score (99.1%), Mean Absolute Error (MAE) (3.7), and Root Mean Square Error (RMSE) (3.3). According to these findings, the model is a powerful instrument for data-driven analysis and quality enhancement in higher education because of its strong generalization capacity, scalability to huge data, and superior interpretability.
21.
N, R.; Srimanickam, B.; K, E.; P, S.; Mehar, K.; Singh, R. P.; K, K. P.
Performance evaluation and machine learning-based prediction of PCM-integrated solar chimney drying for black dates Journal Article
In: Results in Engineering, vol. 28, 2025, ISBN: 25901230 (ISSN), (0).
@article{690,
title = {Performance evaluation and machine learning-based prediction of PCM-integrated solar chimney drying for black dates},
author = {R. N and B. Srimanickam and E. K and S. P and K. Mehar and R. P. Singh and K. P. K},
url = {https://www.sciencedirect.com/science/article/pii/S2590123025042641?via%3Dihub},
doi = {10.1016/j.rineng.2025.108218},
isbn = {25901230 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Results in Engineering},
volume = {28},
publisher = {Elsevier B.V.},
abstract = {This study presents a comparative evaluation of black date drying using three solar-based methods: open sun drying, a solar chimney dryer, and a solar chimney dryer integrated with phase change material (PCM). In parallel, machine learning (ML) models were employed to predict and optimize system performance. Experimental findings reveal that the PCM-integrated solar chimney significantly outperformed conventional approaches, achieving peak thermal and drying efficiencies of 49 % and 59 %, respectively, compared to 20 % for open sun drying and 41 % for the standalone solar chimney. The latent heat storage of PCM extended effective drying into late hours, sustaining 25 % efficiency at 16:00 h against only 11 % under open sun drying. Among the tested ML models—multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR)—the MLP demonstrated the highest predictive accuracy (training: RMSE = 0.85, R² = 0.92; testing: RMSE = 1.10, R² = 0.90). Feature importance analysis further identified solar irradiance and airflow as dominant parameters governing drying performance. By integrating PCM-based thermal management with AI-driven prediction, this work establishes a scalable, energy-efficient drying solution to mitigate agricultural post-harvest losses, directly supporting global initiatives on sustainable food processing and renewable energy utilization.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This study presents a comparative evaluation of black date drying using three solar-based methods: open sun drying, a solar chimney dryer, and a solar chimney dryer integrated with phase change material (PCM). In parallel, machine learning (ML) models were employed to predict and optimize system performance. Experimental findings reveal that the PCM-integrated solar chimney significantly outperformed conventional approaches, achieving peak thermal and drying efficiencies of 49 % and 59 %, respectively, compared to 20 % for open sun drying and 41 % for the standalone solar chimney. The latent heat storage of PCM extended effective drying into late hours, sustaining 25 % efficiency at 16:00 h against only 11 % under open sun drying. Among the tested ML models—multilayer perceptron (MLP), random forest (RF), and support vector regression (SVR)—the MLP demonstrated the highest predictive accuracy (training: RMSE = 0.85, R² = 0.92; testing: RMSE = 1.10, R² = 0.90). Feature importance analysis further identified solar irradiance and airflow as dominant parameters governing drying performance. By integrating PCM-based thermal management with AI-driven prediction, this work establishes a scalable, energy-efficient drying solution to mitigate agricultural post-harvest losses, directly supporting global initiatives on sustainable food processing and renewable energy utilization.
22.
Veerasimman, V.; Prasath, K.; Rajendran, S.; Yang, Y. -L.; Sivanandam, S.
Impact of additive manufacturing techniques on metal based composites: a brief review Journal Article
In: Discover Materials, vol. 5, 2025, ISBN: 27307727 (ISSN), (0).
@article{692,
title = {Impact of additive manufacturing techniques on metal based composites: a brief review},
author = {V. Veerasimman and K. Prasath and S. Rajendran and Y. -L. Yang and S. Sivanandam},
url = {https://link.springer.com/article/10.1007/s43939-025-00432-2},
doi = {10.1007/s43939-025-00432-2},
isbn = {27307727 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Discover Materials},
volume = {5},
publisher = {Discover},
abstract = {This review examines the fundamental principles, processes, and methods of metal-based 3D printing, also known as additive manufacturing (AM), which constructs parts layer by layer directly from digital models. Metal AM enables the fabrication of fully dense metallic components with high precision and speed, making it highly suitable for producing complex, high-performance parts across mechanical, civil, and medical industries. Among various AM technologies, powder bed fusion (PBF) is a leading method, which selectively fuses powder layers using an energy source such as a laser. PBF offers notable advantages in terms of material stability, mechanical durability, and cost-effective production. Unlike existing reviews that typically focus on either specific process techniques or application areas, this manuscript provides a comprehensive and holistic perspective by integrating process mechanisms, material considerations, post-processing effects, and industrial applications. By critically analyzing previous research, this review addresses current limitations, such as parameter optimization and mechanical property enhancement, and highlights emerging opportunities, including the development of efficient, low-cost production strategies. This review thereby provides a clear direction for future research and advancements in metal additive manufacturing technologies.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This review examines the fundamental principles, processes, and methods of metal-based 3D printing, also known as additive manufacturing (AM), which constructs parts layer by layer directly from digital models. Metal AM enables the fabrication of fully dense metallic components with high precision and speed, making it highly suitable for producing complex, high-performance parts across mechanical, civil, and medical industries. Among various AM technologies, powder bed fusion (PBF) is a leading method, which selectively fuses powder layers using an energy source such as a laser. PBF offers notable advantages in terms of material stability, mechanical durability, and cost-effective production. Unlike existing reviews that typically focus on either specific process techniques or application areas, this manuscript provides a comprehensive and holistic perspective by integrating process mechanisms, material considerations, post-processing effects, and industrial applications. By critically analyzing previous research, this review addresses current limitations, such as parameter optimization and mechanical property enhancement, and highlights emerging opportunities, including the development of efficient, low-cost production strategies. This review thereby provides a clear direction for future research and advancements in metal additive manufacturing technologies.
23.
Rajakumar, M. P.; Kumar, Senthil; Srimanickam, B.; Srividhya, S.; Elangovan, K.; Kaliappan, N.; Priya, Kamakshi
Performance enhancement of photovoltaic thermal collectors using water based MnO2 nanofluids and machine learning models Journal Article
In: Scientific Reports, vol. 15, 2025, ISBN: 20452322 (ISSN), (0).
@article{693,
title = {Performance enhancement of photovoltaic thermal collectors using water based MnO2 nanofluids and machine learning models},
author = {M. P. Rajakumar and Senthil Kumar and B. Srimanickam and S. Srividhya and K. Elangovan and N. Kaliappan and Kamakshi Priya},
url = {https://www.nature.com/articles/s41598-025-23505-x},
doi = {10.1038/s41598-025-23505-x},
isbn = {20452322 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Scientific Reports},
volume = {15},
publisher = {Nature Research},
abstract = {This study investigates the enhancement of Photovoltaic-Thermal (PVT) collector performance through the combined use of water-based manganese dioxide (MnO<inf>2</inf>) nanofluids and machine learning (ML) models. Conventional PVT systems often suffer from elevated operating temperatures that degrade photovoltaic efficiency. To address this challenge, the research employs MnO<inf>2</inf> nanoparticles—known for their stability, cost-effectiveness, and high thermal conductivity—dispersed in water to improve thermal regulation within the PVT system. Experimental evaluations were conducted at three flow rates (0.5, 1.0, and 1.5 LPM) to assess thermal and electrical performance. The MnO<inf>2</inf> nanofluid-based PVT collector demonstrated superior power output (ranging from 80.42 W to 202.91 W) compared to water-cooled PVT (72.48 W to 176.17 W) and standalone PV systems (64.23 W to 152.36 W). A peak electrical efficiency of 14.58% was observed at 0.5 LPM, while glazing surface temperatures during midday ranged between 52.03 °C and 54.60 °C, indicating effective thermal management. To predict system behavior and performance, machine learning models—including Random Forest (RF), Radial Basis Function (RBF), and Multilayer Perceptron (MLP)—were applied. Among these, the RBF model achieved the highest predictive accuracy, with R2 values of 0.96 for power output and 0.97 for electrical efficiency on the testing dataset. Overall, this integrated experimental-ML approach not only confirms the thermal and electrical advantages of MnO<inf>2</inf> nanofluids but also demonstrates the potential for intelligent optimization and control in high-performance solar energy systems.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
This study investigates the enhancement of Photovoltaic-Thermal (PVT) collector performance through the combined use of water-based manganese dioxide (MnO<inf>2</inf>) nanofluids and machine learning (ML) models. Conventional PVT systems often suffer from elevated operating temperatures that degrade photovoltaic efficiency. To address this challenge, the research employs MnO<inf>2</inf> nanoparticles—known for their stability, cost-effectiveness, and high thermal conductivity—dispersed in water to improve thermal regulation within the PVT system. Experimental evaluations were conducted at three flow rates (0.5, 1.0, and 1.5 LPM) to assess thermal and electrical performance. The MnO<inf>2</inf> nanofluid-based PVT collector demonstrated superior power output (ranging from 80.42 W to 202.91 W) compared to water-cooled PVT (72.48 W to 176.17 W) and standalone PV systems (64.23 W to 152.36 W). A peak electrical efficiency of 14.58% was observed at 0.5 LPM, while glazing surface temperatures during midday ranged between 52.03 °C and 54.60 °C, indicating effective thermal management. To predict system behavior and performance, machine learning models—including Random Forest (RF), Radial Basis Function (RBF), and Multilayer Perceptron (MLP)—were applied. Among these, the RBF model achieved the highest predictive accuracy, with R2 values of 0.96 for power output and 0.97 for electrical efficiency on the testing dataset. Overall, this integrated experimental-ML approach not only confirms the thermal and electrical advantages of MnO<inf>2</inf> nanofluids but also demonstrates the potential for intelligent optimization and control in high-performance solar energy systems.
24.
Bharath, L.; Kumaraswamy, J.; Manjunatha, T. V.; Suchendra, K. R.
Investigation on Microstructure and Wear Study on Al-Zn-Mg Alloy Hybrid Composites Fabricated Through Die Casting Process Journal Article
In: Evergreen, vol. 11, pp. 3069-3077,, 2024, ISBN: 21890420 (ISSN), (0).
@article{356,
title = {Investigation on Microstructure and Wear Study on Al-Zn-Mg Alloy Hybrid Composites Fabricated Through Die Casting Process},
author = {L. Bharath and J. Kumaraswamy and T. V. Manjunatha and K. R. Suchendra},
url = {https://www.tj.kyushu-u.ac.jp/evergreen/contents/EG2024-11_4_content/pdf/p3069-3077.pdf},
isbn = {21890420 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Evergreen},
volume = {11},
pages = {3069-3077,},
publisher = {Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy},
abstract = {The Al-Zn-Mg hybrid composite play a vibrant role in meeting the definite application in aerospace due to its greater mechanical and tribological properties. In this paper, a die casting technique is adopted to prepare the Al-Zn-Mg hybrid metal matrix composites by changing graphite (Gr.) at 1%, 3%, 5% and 7 weight percentage at constant 2% of silicon carbide (SiC). The test specimens are prepared as per ASTM standard and undergo surface study and wear study. Wear rate for Al-Zn-Mg hybrid metal matrix composite is performed on pin on disc wear equipment by varying sliding distance (250, 500, 750, 1000, 1250 and 1500 m) and applied load (5, 10, 15, 20, 25 and 30 N) at constant sliding velocity (1.5 m/sec). Optical microstructure images reveal sufficient bonding between matrix and reinforcement material. As graphite particle increases wear rate observed to be decreased however, rising in speed and load wear rate was increased. For Al-Zn-Mg/2% SiC/7% Gr. hybrid composite exhibited 57.83% decrease in wear rate has compared to non-reinforced Al-Zn-Mg alloy. SEM images of worn-out surface shows scratching, ploughing, delaminated layer and plastic deformation.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The Al-Zn-Mg hybrid composite play a vibrant role in meeting the definite application in aerospace due to its greater mechanical and tribological properties. In this paper, a die casting technique is adopted to prepare the Al-Zn-Mg hybrid metal matrix composites by changing graphite (Gr.) at 1%, 3%, 5% and 7 weight percentage at constant 2% of silicon carbide (SiC). The test specimens are prepared as per ASTM standard and undergo surface study and wear study. Wear rate for Al-Zn-Mg hybrid metal matrix composite is performed on pin on disc wear equipment by varying sliding distance (250, 500, 750, 1000, 1250 and 1500 m) and applied load (5, 10, 15, 20, 25 and 30 N) at constant sliding velocity (1.5 m/sec). Optical microstructure images reveal sufficient bonding between matrix and reinforcement material. As graphite particle increases wear rate observed to be decreased however, rising in speed and load wear rate was increased. For Al-Zn-Mg/2% SiC/7% Gr. hybrid composite exhibited 57.83% decrease in wear rate has compared to non-reinforced Al-Zn-Mg alloy. SEM images of worn-out surface shows scratching, ploughing, delaminated layer and plastic deformation.
25.
Chandrasekhar, G. L.; Vijayakumar, Y.; Nagaral, M.; Rajesh, A.; Manjunath, K.; Kaviti, R. V. P.; Auradi, V.
Synthesis and tensile behavior of Al7475-nano B4C particles reinforced composites at elevated temperatures Journal Article
In: Materials Physics and Mechanics, vol. 52, pp. 44-57,, 2024, ISBN: 16052730 (ISSN), (0).
@article{31,
title = {Synthesis and tensile behavior of Al7475-nano B4C particles reinforced composites at elevated temperatures},
author = {G. L. Chandrasekhar and Y. Vijayakumar and M. Nagaral and A. Rajesh and K. Manjunath and R. V. P. Kaviti and V. Auradi},
doi = {10.18149/MPM.5232024_5},
isbn = {16052730 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Materials Physics and Mechanics},
volume = {52},
pages = {44-57,},
publisher = {Institute for Problems in Mechanical Engineering, Russian Academy of Sciences},
abstract = {Materials with superior mechanical and wear properties, high strength, high stiffness, and low weight are necessary for modern technology. Mechanical characteristics of metal matrix composites are crucial to their potential use as structural materials. The current research focuses on the preparation of Al7475 alloy with 400 to 500 nm sized B4C a composite using a liquid metallurgy technique. Al7475 alloy was used to make composites with 2, 4, 6, 8 and 10 wt. % of B4C particles. Microstructural analysis was performed on the produced composites using SEM and EDS. Density, hardness, ultimate strength, yield strength, and elongation as a percentage were all measured as per ASTM norms. Further, tensile tests were conducted at room temperature, 50 and 100 °C elevated temperatures. SEM images showed that the boron carbide particles were evenly dispersed throughout the Al7475 alloy. EDS spectrums verified that Al7475 alloy contains boron carbide particles. By incorporating dual particles into the matrix, the density of Al alloy composites was lowered. Al7475 alloy with B4C composites exhibited superior tensile properties at room and elevated temperatures as compared to the base alloy. © G.L. Chandrasekhar, Y. Vijayakumar, M. Nagaral, A. Rajesh, K. Manjunath, R. Vara Prasad Kaviti, Virupaxi Auradi, 2024.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Materials with superior mechanical and wear properties, high strength, high stiffness, and low weight are necessary for modern technology. Mechanical characteristics of metal matrix composites are crucial to their potential use as structural materials. The current research focuses on the preparation of Al7475 alloy with 400 to 500 nm sized B4C a composite using a liquid metallurgy technique. Al7475 alloy was used to make composites with 2, 4, 6, 8 and 10 wt. % of B4C particles. Microstructural analysis was performed on the produced composites using SEM and EDS. Density, hardness, ultimate strength, yield strength, and elongation as a percentage were all measured as per ASTM norms. Further, tensile tests were conducted at room temperature, 50 and 100 °C elevated temperatures. SEM images showed that the boron carbide particles were evenly dispersed throughout the Al7475 alloy. EDS spectrums verified that Al7475 alloy contains boron carbide particles. By incorporating dual particles into the matrix, the density of Al alloy composites was lowered. Al7475 alloy with B4C composites exhibited superior tensile properties at room and elevated temperatures as compared to the base alloy. © G.L. Chandrasekhar, Y. Vijayakumar, M. Nagaral, A. Rajesh, K. Manjunath, R. Vara Prasad Kaviti, Virupaxi Auradi, 2024.
26.
Totad, V. S.; Vishwanatha, J. S.; Yaliwal, V. S.; Desai, A.; Banapurmath, N. R.; Harari, P. A.; Kulkarni, S.
Hydrogen and Hydrogen-Blended Mahua Biodiesel Comparative Investigation in CI Engines Operating Under HCCI Combustion Mode with EGR Variation Journal Article
In: Advances in Science, Technology and Innovation, vol. 2024, pp. 299-304,, 2024, ISBN: 25228714 (ISSN), (0).
@article{349,
title = {Hydrogen and Hydrogen-Blended Mahua Biodiesel Comparative Investigation in CI Engines Operating Under HCCI Combustion Mode with EGR Variation},
author = {V. S. Totad and J. S. Vishwanatha and V. S. Yaliwal and A. Desai and N. R. Banapurmath and P. A. Harari and S. Kulkarni},
url = {https://link.springer.com/chapter/10.1007/978-3-031-63909-8_41},
doi = {10.1007/978-3-031-63909-8_41},
isbn = {25228714 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Advances in Science, Technology and Innovation},
volume = {2024},
pages = {299-304,},
publisher = {Springer Nature},
abstract = {To ensure the sustainable energy demand of the world to meet the standard of living mankind, researchers are in search of new renewable fuels which can replace the fossil fuels those are going to be depleted soon and at the same time saving of environment. Given their many advantages over fossil fuels, the adoption of novel substitute fuels for diesel engine utilization is gaining widespread popularity. They provide both food and energy security, reduce reliance on foreign currencies, and possess the advantages of renewability and biodegradability. Thus replacing the liquid fossil fuels in diesel engine with alternative or renewable fuels; can lower the emission levels to some reasonable extent. They address socioeconomic problems as well as environmental ones. Although compression ignition engines offer various advantages over spark ignition engines, they tend to generate higher levels of smoke and nitrogen oxides. In this present research, an effort has been made to reduce the levels of both smoke and nitric oxide emissions. To attain this, diesel engine tested under homogeneous charge compression ignition mode of combustion is a superior selection. The study delves into the impact of hydrogen (H2) and Mahua oil methyl ester (MOME) on the thermal efficiency and emission characteristics of a compression ignition engine operating in the Homogeneous Charge Compression Ignition (HCCI) mode. This research also involves implementing part of burnt gas re-entrance (EGR) to assess the performance of HCCI engines.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
To ensure the sustainable energy demand of the world to meet the standard of living mankind, researchers are in search of new renewable fuels which can replace the fossil fuels those are going to be depleted soon and at the same time saving of environment. Given their many advantages over fossil fuels, the adoption of novel substitute fuels for diesel engine utilization is gaining widespread popularity. They provide both food and energy security, reduce reliance on foreign currencies, and possess the advantages of renewability and biodegradability. Thus replacing the liquid fossil fuels in diesel engine with alternative or renewable fuels; can lower the emission levels to some reasonable extent. They address socioeconomic problems as well as environmental ones. Although compression ignition engines offer various advantages over spark ignition engines, they tend to generate higher levels of smoke and nitrogen oxides. In this present research, an effort has been made to reduce the levels of both smoke and nitric oxide emissions. To attain this, diesel engine tested under homogeneous charge compression ignition mode of combustion is a superior selection. The study delves into the impact of hydrogen (H2) and Mahua oil methyl ester (MOME) on the thermal efficiency and emission characteristics of a compression ignition engine operating in the Homogeneous Charge Compression Ignition (HCCI) mode. This research also involves implementing part of burnt gas re-entrance (EGR) to assess the performance of HCCI engines.
27.
Bharath, L.; Kumaraswamy, J.; Manjunath, T. V.; Kulkarni, S. K. N.
In: Multiscale and Multidisciplinary Modeling, Experiments and Design, vol. 7, pp. 5387-5399,, 2024, ISBN: 25208179 (ISSN), (5).
@article{6,
title = {Evaluation of microstructure and prediction of hardness of Al–Cu based composites by using artificial neural network and linear regression through machine learning technique},
author = {L. Bharath and J. Kumaraswamy and T. V. Manjunath and S. K. N. Kulkarni},
doi = {10.1007/s41939-024-00525-0},
isbn = {25208179 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Multiscale and Multidisciplinary Modeling, Experiments and Design},
volume = {7},
pages = {5387-5399,},
publisher = {Springer Science and Business Media B.V.},
abstract = {Al–Cu alloy with B4C particulates will meet the specific application which includes window panel, seats, aircraft structure and aircraft fittings due to their excellent mechanical properties. In this paper, Al–Cu/B4C composites was fabricated by using three parameters (wt% of B4C, ageing duration and mesh size) with three level each as per the design of experiments. Al–Cu/B4C composites were machined as per IS:1500 standard to evaluate hardness by experimental method. An Artificial Neural Network model was developed by using the Levenberg–Marquardt algorithm to predict the experimental hardness value of formed composites. Linear Regression model is created and evaluated by taking 30% of experimental data set for testing and 70% for training. Polynomial feature is imported with only 2° with their interaction only. It is seen that the established ANN model predicts the closeness with the experimental hardness within ± 10% error. It is seen that 14.58% improvement was been observed after considering polynomial feature for the linear regression model. In addition, microstructure study was discussed for the fabricated composites as per IS:7739 standard and observed that B4C particles were homogeneously dispersed in the Al–Cu based matrix and exhibit good bonding between them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.},
note = {5},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Al–Cu alloy with B4C particulates will meet the specific application which includes window panel, seats, aircraft structure and aircraft fittings due to their excellent mechanical properties. In this paper, Al–Cu/B4C composites was fabricated by using three parameters (wt% of B4C, ageing duration and mesh size) with three level each as per the design of experiments. Al–Cu/B4C composites were machined as per IS:1500 standard to evaluate hardness by experimental method. An Artificial Neural Network model was developed by using the Levenberg–Marquardt algorithm to predict the experimental hardness value of formed composites. Linear Regression model is created and evaluated by taking 30% of experimental data set for testing and 70% for training. Polynomial feature is imported with only 2° with their interaction only. It is seen that the established ANN model predicts the closeness with the experimental hardness within ± 10% error. It is seen that 14.58% improvement was been observed after considering polynomial feature for the linear regression model. In addition, microstructure study was discussed for the fabricated composites as per IS:7739 standard and observed that B4C particles were homogeneously dispersed in the Al–Cu based matrix and exhibit good bonding between them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
28.
Suhas, U.; Shashidhara, K. N.; Raghavendra, M. J.; Billady, R. K.; Balaji, S.
Optimizing Wear Characteristics of Aluminium Powder Reinforced Epoxy Polymer Matrix Composite Using Taguchi Grey Relational Analysis Approach Journal Article
In: Journal of The Institution of Engineers (India): Series D, 2024, ISBN: 22502122 (ISSN), (0).
@article{73,
title = {Optimizing Wear Characteristics of Aluminium Powder Reinforced Epoxy Polymer Matrix Composite Using Taguchi Grey Relational Analysis Approach},
author = {U. Suhas and K. N. Shashidhara and M. J. Raghavendra and R. K. Billady and S. Balaji},
doi = {10.1007/s40033-024-00768-8},
isbn = {22502122 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of The Institution of Engineers (India): Series D},
publisher = {Springer},
abstract = {Aluminium-epoxy composites, known for their improved mechanical and tribological properties, exhibit significant potential in various engineering applications. This study investigates the wear behaviour of these composites with a focus on optimization for improved performance. An appropriate manufacturing approach was utilized in order to incorporate aluminum particles into an epoxy matrix as part of the fabrication process. An apparatus that was specifically designed for testing was utilized in order to assess the wear characteristics. For the purpose of designing trials and optimizing the parameters of the wear process, Taguchi's approach was applied. Zircon particles were found to be distributed uniformly throughout the Al-epoxy composites, as demonstrated by the visual evidence obtained from scanning microscopy samples. Statistical analysis revealed that the factor load is the most significant contribution to wear, accounting for roughly 5.67% of the observed wear. Sliding distance, speed, and composite load were determined to be the next most significant contributors to wear. Finding the COF in composites can be accomplished by the use of a prediction tool that is the regression equation that was generated from the data. According to the GRA response table, the best set of parameters for improving the wear properties is as follows: the applied load should be 35 N, the sliding speed should be 250 m/s, and the sliding distance should be approximately 1650 m. A scanning electron microscopy examination of the surface that had been worn revealed that the Al-epoxy composite had exhibited steady wear with the parameters adjusted to their ideal values. © The Institution of Engineers (India) 2024.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
Aluminium-epoxy composites, known for their improved mechanical and tribological properties, exhibit significant potential in various engineering applications. This study investigates the wear behaviour of these composites with a focus on optimization for improved performance. An appropriate manufacturing approach was utilized in order to incorporate aluminum particles into an epoxy matrix as part of the fabrication process. An apparatus that was specifically designed for testing was utilized in order to assess the wear characteristics. For the purpose of designing trials and optimizing the parameters of the wear process, Taguchi's approach was applied. Zircon particles were found to be distributed uniformly throughout the Al-epoxy composites, as demonstrated by the visual evidence obtained from scanning microscopy samples. Statistical analysis revealed that the factor load is the most significant contribution to wear, accounting for roughly 5.67% of the observed wear. Sliding distance, speed, and composite load were determined to be the next most significant contributors to wear. Finding the COF in composites can be accomplished by the use of a prediction tool that is the regression equation that was generated from the data. According to the GRA response table, the best set of parameters for improving the wear properties is as follows: the applied load should be 35 N, the sliding speed should be 250 m/s, and the sliding distance should be approximately 1650 m. A scanning electron microscopy examination of the surface that had been worn revealed that the Al-epoxy composite had exhibited steady wear with the parameters adjusted to their ideal values. © The Institution of Engineers (India) 2024.
29.
Basavaraj, K.; Elangovan, K.; Shankar, S.
Effects of Al2O3 Concentration in Ethylene Glycol on Convection Heat Transfer Coefficient Journal Article
In: International Journal of Vehicle Structures and Systems, vol. 15, pp. 345-350,, 2023, ISBN: 09753060 (ISSN), (0).
@article{48,
title = {Effects of Al2O3 Concentration in Ethylene Glycol on Convection Heat Transfer Coefficient},
author = {K. Basavaraj and K. Elangovan and S. Shankar},
doi = {10.4273/ijvss.15.3.12},
isbn = {09753060 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Vehicle Structures and Systems},
volume = {15},
pages = {345-350,},
publisher = {MechAero Found. for Techn. Res. and Educ. Excellence},
abstract = {The energy demand is more in the world due to increase in the populations. The sustainable and clean renewable energy is required to meet the demand. The solar energy with nanofluid used as heat transfer medium is the best alternative source to enhance the rate of heat transfer. The nanofluids are the suspended nano sized particles in the water, ethylene glycol or oil. The stability analysis of Al2O3 ethylene glycol carried out using zeta potential method. The 20nm sized Al2O3 nanoparticles with volume concentration from 0.01% to 1% in ethylene glycol is used as nanofluid to study the effects of concentration on convective heat transfer coefficient (HTC) and wall function HTC at temperature 298K and mass flow rate 0.033kg/s. The investigation also carried out to study the effects of concentration on its thermophysical properties of nanofluid. ANSYS fluent software used to carry out the numerical analysis with suitable thermal boundary conditions. © 2023. Carbon Magics Ltd.},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The energy demand is more in the world due to increase in the populations. The sustainable and clean renewable energy is required to meet the demand. The solar energy with nanofluid used as heat transfer medium is the best alternative source to enhance the rate of heat transfer. The nanofluids are the suspended nano sized particles in the water, ethylene glycol or oil. The stability analysis of Al2O3 ethylene glycol carried out using zeta potential method. The 20nm sized Al2O3 nanoparticles with volume concentration from 0.01% to 1% in ethylene glycol is used as nanofluid to study the effects of concentration on convective heat transfer coefficient (HTC) and wall function HTC at temperature 298K and mass flow rate 0.033kg/s. The investigation also carried out to study the effects of concentration on its thermophysical properties of nanofluid. ANSYS fluent software used to carry out the numerical analysis with suitable thermal boundary conditions. © 2023. Carbon Magics Ltd.
30.
Basavaraj, K.; Elangovan, K.; Shankar, S.; Kulkarni, A.
Effects of Reynolds Number on the Volume Concentration of Al2O3 in Ethylene Glycol Journal Article
In: International Journal of Vehicle Structures and Systems, vol. 15, pp. 351-355,, 2023, ISBN: 09753060 (ISSN), (1).
@article{79,
title = {Effects of Reynolds Number on the Volume Concentration of Al2O3 in Ethylene Glycol},
author = {K. Basavaraj and K. Elangovan and S. Shankar and A. Kulkarni},
doi = {10.4273/ijvss.15.3.13},
isbn = {09753060 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Vehicle Structures and Systems},
volume = {15},
pages = {351-355,},
publisher = {MechAero Found. for Techn. Res. and Educ. Excellence},
abstract = {The nanofluid is suspension of nanoparticles in the base fluid used to enhance the heat transfer rate in many applications of heat transfer. The Reynolds number is the dimensionless number used to characterize the flow of the nanofluid to enhance the heat transfer rate. The current study, the effects of Reynolds number on 0.01% volume concentrations of the aluminium oxide (Al2O3) nanoparticles in the ethylene glycol through the flow in the pipe are investigated. In the study, ethylene glycol is considered as the base fluid. The thermophysical characteristics of the Al2O3/EG nanofluid were determined in the current work using a cylindrical copper pipe with an 8 mm diameter and 400 mm length. The heat transfer coefficient (HTC) and Nusselt number were calculated for the Reynolds number varied from 200 to 1200, considering nanofluid inlet temperature of 298 K and constant heat flux at the wall surface of pipe 1100 W/m2 K. The findings showed that the HTC and Nusselt number of the nanofluid flow in the pipe increased as the Reynolds number increased. Further, an exponential correlation was proposed for variation of the Reynolds number with HTC and Nusselt number for the obtained results. ANSYS Fluent software was used for the analysis of the nanofluid flow though pipe. © 2023. Carbon Magics Ltd.},
note = {1},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The nanofluid is suspension of nanoparticles in the base fluid used to enhance the heat transfer rate in many applications of heat transfer. The Reynolds number is the dimensionless number used to characterize the flow of the nanofluid to enhance the heat transfer rate. The current study, the effects of Reynolds number on 0.01% volume concentrations of the aluminium oxide (Al2O3) nanoparticles in the ethylene glycol through the flow in the pipe are investigated. In the study, ethylene glycol is considered as the base fluid. The thermophysical characteristics of the Al2O3/EG nanofluid were determined in the current work using a cylindrical copper pipe with an 8 mm diameter and 400 mm length. The heat transfer coefficient (HTC) and Nusselt number were calculated for the Reynolds number varied from 200 to 1200, considering nanofluid inlet temperature of 298 K and constant heat flux at the wall surface of pipe 1100 W/m2 K. The findings showed that the HTC and Nusselt number of the nanofluid flow in the pipe increased as the Reynolds number increased. Further, an exponential correlation was proposed for variation of the Reynolds number with HTC and Nusselt number for the obtained results. ANSYS Fluent software was used for the analysis of the nanofluid flow though pipe. © 2023. Carbon Magics Ltd.
31.
Sagar, K. G.; Suresh, P. M.; Sampathkumaran, P.
Tribological studies on aluminum beryl composites subjected to ECAP process Journal Article
In: Wear, vol. 523, pp. 204775+, 2023, ISBN: 00431648 (ISSN), (4).
@article{96,
title = {Tribological studies on aluminum beryl composites subjected to ECAP process},
author = {K. G. Sagar and P. M. Suresh and P. Sampathkumaran},
doi = {10.1016/j.wear.2023.204775},
isbn = {00431648 (ISSN)},
year = {2023},
date = {2023-01-01},
journal = {Wear},
volume = {523},
pages = {204775+},
publisher = {Elsevier Ltd},
abstract = {The strengthening processes of aluminum alloys and their composites are generally assessed after precipitation hardening or following different laid down strain hardening procedures such as Equal Channel Angular Pressing (ECAP), Multi-Axial Forging (MAF), High-Pressure Torsion (HPT), Accumulative Roll Bonding (ARB), etc. Among them, ECAP is considered to be a novel method of producing fine-grained structure with different properties. On-going through the available literature, it is noted that the adoption of ECAP in Aluminum metal matrix composites, especially the Aluminum Beryl composites and then assessing their tribological responses, are scarce to come across. Hence, this particular aspect is addressed in this work. In the present investigation, Al 2024 alloys containing 6% Beryl particles, produced by stir cast route and subjected to ECAP operation up to two passes, have been assessed for slide wear and coefficient of friction (CoF) properties for comparison with un-reinforced Al 2024 alloy for qualitative and quantitative evaluation purposes. The wear and friction evaluation are carried out using the well-known Pin-On-Disc (POD) apparatus for three different loads (49 N, 91 N, and 136 N) and sliding distances (2000, 4000 and 6000 m) at a constant sliding speed of 1.67 m/s. The data reveals that the wear loss decreases with increase in the number of passes due to increase in hardness & decrease in grain size for all the three loads and sliding distances employed. Further, the wear losses show an increasing trend with an increase in the applied load. Also, it is observed that the CoF decreases with increase in number of passes at all loads. It is high in the as cast state and it increases with increase in load. It is inferred from the present findings that the ECAP has made a significant impact on the wear behaviour of Al-Beryl composites, thus emphasizing the fact that the composites are suited for wear resistance applications such as in production engineering sectors. © 2023 Elsevier B.V.},
note = {4},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The strengthening processes of aluminum alloys and their composites are generally assessed after precipitation hardening or following different laid down strain hardening procedures such as Equal Channel Angular Pressing (ECAP), Multi-Axial Forging (MAF), High-Pressure Torsion (HPT), Accumulative Roll Bonding (ARB), etc. Among them, ECAP is considered to be a novel method of producing fine-grained structure with different properties. On-going through the available literature, it is noted that the adoption of ECAP in Aluminum metal matrix composites, especially the Aluminum Beryl composites and then assessing their tribological responses, are scarce to come across. Hence, this particular aspect is addressed in this work. In the present investigation, Al 2024 alloys containing 6% Beryl particles, produced by stir cast route and subjected to ECAP operation up to two passes, have been assessed for slide wear and coefficient of friction (CoF) properties for comparison with un-reinforced Al 2024 alloy for qualitative and quantitative evaluation purposes. The wear and friction evaluation are carried out using the well-known Pin-On-Disc (POD) apparatus for three different loads (49 N, 91 N, and 136 N) and sliding distances (2000, 4000 and 6000 m) at a constant sliding speed of 1.67 m/s. The data reveals that the wear loss decreases with increase in the number of passes due to increase in hardness & decrease in grain size for all the three loads and sliding distances employed. Further, the wear losses show an increasing trend with an increase in the applied load. Also, it is observed that the CoF decreases with increase in number of passes at all loads. It is high in the as cast state and it increases with increase in load. It is inferred from the present findings that the ECAP has made a significant impact on the wear behaviour of Al-Beryl composites, thus emphasizing the fact that the composites are suited for wear resistance applications such as in production engineering sectors. © 2023 Elsevier B.V.
32.
Karthik, R.; Elangovan, K.; Girisha, K. G.; Shankar, S.
Elsevier Ltd, vol. 62, 2022, ISBN: 22147853 (ISSN), (1).
@proceedings{189,
title = {Experimental investigation on wear behaviour of laser sintered and unsintered Inconel 718 in vibratory finishing},
author = {R. Karthik and K. Elangovan and K. G. Girisha and S. Shankar},
doi = {10.1016/j.matpr.2022.04.201},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {62},
pages = {2339-2346,},
publisher = {Elsevier Ltd},
abstract = {Vibratory finishing is a mass finishing process employed in the industries to deburr, clean and fabricate an isotropic finishing on metallic materials. In this process the manual involvement is self-effacing. In the present investigation the tribological behaviour of the laser sintered and Unsintered Inconel 718 components due to comparative motion between the samples and media have been investigated. The ceramic media is the extensively favourite media type. Scanning electron microscope and EDAX spectrum analysis was used to analyse the surface morphology and chemical composition of the components respectively. The wear rate was measured on the basis of weight loss of the components with respect to media. The effect of media and test duration on the wear behaviour was analysed and reported. The surface micro hardness and surface roughness of the samples was investigated. The investigated results indicated that due superior surface hardness and density in the sintering, the sintered samples shows extreme wear resistance as compared to unsintered samples. © 2022},
note = {1},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Vibratory finishing is a mass finishing process employed in the industries to deburr, clean and fabricate an isotropic finishing on metallic materials. In this process the manual involvement is self-effacing. In the present investigation the tribological behaviour of the laser sintered and Unsintered Inconel 718 components due to comparative motion between the samples and media have been investigated. The ceramic media is the extensively favourite media type. Scanning electron microscope and EDAX spectrum analysis was used to analyse the surface morphology and chemical composition of the components respectively. The wear rate was measured on the basis of weight loss of the components with respect to media. The effect of media and test duration on the wear behaviour was analysed and reported. The surface micro hardness and surface roughness of the samples was investigated. The investigated results indicated that due superior surface hardness and density in the sintering, the sintered samples shows extreme wear resistance as compared to unsintered samples. © 2022
33.
Karthik, R.; Elangovan, K.; Shankar, S.; Girisha, K. G.
Elsevier Ltd, vol. 64, 2022, ISBN: 22147853 (ISSN), (4).
@proceedings{154,
title = {An experimental analysis on surface roughness of the selective laser sintered and unsintered Inconel 718 components using vibratory surface finishing process},
author = {R. Karthik and K. Elangovan and S. Shankar and K. G. Girisha},
doi = {10.1016/j.matpr.2022.04.448},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {64},
pages = {220-224,},
publisher = {Elsevier Ltd},
abstract = {The selective laser sintering (SLS) is one among the most fast emerging rapid prototyping processes, primarily due to its capability to produce component using different kind of raw materials. On the other hand, because of stair stepping effect the prototypes produced by SLS technique have moderately high surface roughness. Vibratory finishing is the multifunctional process is utilized in several engineering industries internationally for various applications like fine finishing, brightening, cleaning, and radiusing of components. In the present research work, due to excellent mechanical properties Inconel 718 powder particles were used as raw materials to produce component using SLS technique. The surface roughness of the components (both sintered and unsintered) were analyzed by subjected them to vibratory surface finishing process using different media under constant amplitude of 0.8 mm and frequency of 25 Hz, 50 Hz, and 75 Hz for different processing time in minutes. The surface roughness tester was used to anlyse the surface roughness of the components after vibratory finishing test. The surface morphology was examined using scanning electron microscope and EDAX. The Vicker's micro hardness tester was used to know the surface roughness of the components, laser sintered samples shows extreme hardness as compared to unsintered samples [17]. The experimental results confirm that, the surface roughness of the laser sintered and unsintered samples was reduced at 75 Hz as compared to lower taken frequencies. However, laser sintered sample performed well as compared to unsintered samples due to high density, less porosity and high surface hardness. © 2022},
note = {4},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
The selective laser sintering (SLS) is one among the most fast emerging rapid prototyping processes, primarily due to its capability to produce component using different kind of raw materials. On the other hand, because of stair stepping effect the prototypes produced by SLS technique have moderately high surface roughness. Vibratory finishing is the multifunctional process is utilized in several engineering industries internationally for various applications like fine finishing, brightening, cleaning, and radiusing of components. In the present research work, due to excellent mechanical properties Inconel 718 powder particles were used as raw materials to produce component using SLS technique. The surface roughness of the components (both sintered and unsintered) were analyzed by subjected them to vibratory surface finishing process using different media under constant amplitude of 0.8 mm and frequency of 25 Hz, 50 Hz, and 75 Hz for different processing time in minutes. The surface roughness tester was used to anlyse the surface roughness of the components after vibratory finishing test. The surface morphology was examined using scanning electron microscope and EDAX. The Vicker's micro hardness tester was used to know the surface roughness of the components, laser sintered samples shows extreme hardness as compared to unsintered samples [17]. The experimental results confirm that, the surface roughness of the laser sintered and unsintered samples was reduced at 75 Hz as compared to lower taken frequencies. However, laser sintered sample performed well as compared to unsintered samples due to high density, less porosity and high surface hardness. © 2022
34.
Talikoti, B. H.; Girisha, H. N.; Bharath, L.
Validation of hardness and tensile strength of Al-Mg alloy reinforced with silicon carbide and graphite hybrid composite by regression equation Journal Article
In: Materials Today: Proceedings, vol. 59, pp. 562-567,, 2022, ISBN: 22147853 (ISSN), (0).
@article{134,
title = {Validation of hardness and tensile strength of Al-Mg alloy reinforced with silicon carbide and graphite hybrid composite by regression equation},
author = {B. H. Talikoti and H. N. Girisha and L. Bharath},
doi = {10.1016/j.matpr.2021.12.079},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {59},
pages = {562-567,},
publisher = {Elsevier Ltd},
abstract = {In this article, Al6061 alloy is reinforced with SiC and graphite powder for the fabrication of Al6061/Gr./SiC hybrid composite routed through stir casting technique. The silicon carbide is varied at 2%, 4% and 6 wt% whereas graphite powders is varied at 1%, 3%, 5% and 7 wt% in Al6061 alloy. The cast specimens were machined as per ASTM E8M-15a for tensile test sample and IS 1501 for hardness specimen by using lathe machine tool. Hardness and tensile strength of Al6061/SiC/Gr. hybrid composite increased with increase in wt.% of reinforcement when compared to as received Al6061 alloy. Taguchi Technique have been used to evaluate the optimization, effect of parameter, S/N ratio and General Linear (GL) regression equation were derived for validation of experimental values. Wt.% of graphite have high contribution of about 61.67% and 60.82% for tensile strength and Vicker's hardness. The confirmation result revealed the good agreement with the experimental result for tensile strength and hardness of Al6061/SiC/Gr. hybrid composites. © 2021},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
In this article, Al6061 alloy is reinforced with SiC and graphite powder for the fabrication of Al6061/Gr./SiC hybrid composite routed through stir casting technique. The silicon carbide is varied at 2%, 4% and 6 wt% whereas graphite powders is varied at 1%, 3%, 5% and 7 wt% in Al6061 alloy. The cast specimens were machined as per ASTM E8M-15a for tensile test sample and IS 1501 for hardness specimen by using lathe machine tool. Hardness and tensile strength of Al6061/SiC/Gr. hybrid composite increased with increase in wt.% of reinforcement when compared to as received Al6061 alloy. Taguchi Technique have been used to evaluate the optimization, effect of parameter, S/N ratio and General Linear (GL) regression equation were derived for validation of experimental values. Wt.% of graphite have high contribution of about 61.67% and 60.82% for tensile strength and Vicker's hardness. The confirmation result revealed the good agreement with the experimental result for tensile strength and hardness of Al6061/SiC/Gr. hybrid composites. © 2021
35.
Bharath, L.; Kulkarni, S. N.
Evaluation of UTS and compression strength of Al2024/B4C composites by experimental method and validation through regression analysis Journal Article
In: Materials Today: Proceedings, vol. 59, pp. 25-30,, 2022, ISBN: 22147853 (ISSN), (0).
@article{133,
title = {Evaluation of UTS and compression strength of Al2024/B4C composites by experimental method and validation through regression analysis},
author = {L. Bharath and S. N. Kulkarni},
doi = {10.1016/j.matpr.2021.10.066},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {59},
pages = {25-30,},
publisher = {Elsevier Ltd},
abstract = {The contemporary investigation deals with the formation of Al2024 composites reinforced with boron carbide particulates by varying wt.% (1, 3, and 5%) routed through a liquid metallurgical process. The 3 different mesh sizes say 100, 200, and 300 of B4C particulates have been chosen as reinforcement. Heat treatment has been done for the machined test specimen to know the effect on UTS and compression strength for the formed composites. UTS and compression strength have been evaluated for both Al2024 alloy and Al2024/B4C composites with and without heat treatment conditions. The density of the formed composite material is decreased when the content of reinforcement increases in the Al2024 matrix. The result of this investigation revealed that as wt.% of B4C increased there is a significant increase in the UTS and compression strength, accompanied by a reduction in its ductility. Fracture analysis has been done for the tensile fracture specimen to know the bonding between matrix and reinforcement and type of fracture. Further, an attempt is made to validate experimental results by using regression analysis through Minitab software. © 2022},
note = {0},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
The contemporary investigation deals with the formation of Al2024 composites reinforced with boron carbide particulates by varying wt.% (1, 3, and 5%) routed through a liquid metallurgical process. The 3 different mesh sizes say 100, 200, and 300 of B4C particulates have been chosen as reinforcement. Heat treatment has been done for the machined test specimen to know the effect on UTS and compression strength for the formed composites. UTS and compression strength have been evaluated for both Al2024 alloy and Al2024/B4C composites with and without heat treatment conditions. The density of the formed composite material is decreased when the content of reinforcement increases in the Al2024 matrix. The result of this investigation revealed that as wt.% of B4C increased there is a significant increase in the UTS and compression strength, accompanied by a reduction in its ductility. Fracture analysis has been done for the tensile fracture specimen to know the bonding between matrix and reinforcement and type of fracture. Further, an attempt is made to validate experimental results by using regression analysis through Minitab software. © 2022
36.
Kumar, Shashi; Krishnappa, G. B.
Design and finite element analysis of AISI 4340 alloy steel helical gear Proceedings
Elsevier Ltd, vol. 65, 2022, ISBN: 22147853 (ISSN), (3).
@proceedings{123,
title = {Design and finite element analysis of AISI 4340 alloy steel helical gear},
author = {Shashi Kumar and G. B. Krishnappa},
doi = {10.1016/j.matpr.2022.06.288},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {65},
pages = {3671-3674,},
publisher = {Elsevier Ltd},
abstract = {The present study aims at design, modeling, modal analysis, static analysis and harmonic analysis of helical gear of AISI 4340 material. The dimensions of helical gear are obtained through comprehensive theoretical design procedure. Three dimensional model of helical gear was created by three dimensional modeling software. Ansys workbench finite element tool was used for harmonic analysis, static structural analysis and modal analysis of designed helical gear. Helical gears are commonly used components in power transmission system. Helical gears play a vital role in transmitting motion and power. Gear teeth are usually subjected to root bending stresses and tooth contact stresses. Study of these stresses during designing helical gear is very important. Hence numerical analysis of AISI 4340 helical gear is done using numerical analysis software tool to check the designed gear met the application requirement or not. © 2022},
note = {3},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
The present study aims at design, modeling, modal analysis, static analysis and harmonic analysis of helical gear of AISI 4340 material. The dimensions of helical gear are obtained through comprehensive theoretical design procedure. Three dimensional model of helical gear was created by three dimensional modeling software. Ansys workbench finite element tool was used for harmonic analysis, static structural analysis and modal analysis of designed helical gear. Helical gears are commonly used components in power transmission system. Helical gears play a vital role in transmitting motion and power. Gear teeth are usually subjected to root bending stresses and tooth contact stresses. Study of these stresses during designing helical gear is very important. Hence numerical analysis of AISI 4340 helical gear is done using numerical analysis software tool to check the designed gear met the application requirement or not. © 2022
37.
Sagar, K. G.; Anjani, P. K.; Raman, M. S.; Devi, N. S. M. P. L.; Mehta, K.; Gonzales, J. L. A.; Kumar, N. M.; Venkatesan, S.
Improving Sustainability of EDM Sector by Implementing Unconventional Competitive Manufacturing Approach Journal Article
In: Advances in Materials Science and Engineering, vol. 2022, pp. 6164599+, 2022, ISBN: 16878434 (ISSN), (4).
@article{116,
title = {Improving Sustainability of EDM Sector by Implementing Unconventional Competitive Manufacturing Approach},
author = {K. G. Sagar and P. K. Anjani and M. S. Raman and N. S. M. P. L. Devi and K. Mehta and J. L. A. Gonzales and N. M. Kumar and S. Venkatesan},
doi = {10.1155/2022/6164599},
isbn = {16878434 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Advances in Materials Science and Engineering},
volume = {2022},
pages = {6164599+},
publisher = {Hindawi Limited},
abstract = {In this research work, an attempt was made to machine the titanium (Ti6Al4V) alloy utilizing electric discharge machining technique. The distinct process parameters and its impact on the machining performance were identified using the cause-and-effect diagram (CED). The key process parameters identified by CED diagram were current, pulse on time (Ton), aluminium oxide (Al2O3) powder concentration, and gap distance; experiments were conducted by varying the process parameters, experimental runs were designed using the Taguchi mixed orthogonal array. The experimental results revealed that improvement in material removal rate (MRR) was due to the bridging effect; reduction in tool wear rate (TWR) owing to the expansion of spark gap and enhancement in the surface roughness (Ra) was due to the complete flushing of machined debris. The interaction impact was analysed using the contour plot and with the aid of mathematical modelling experimental fits that were identified and the results were validated utilizing the sensitivity analysis. The obtained results were optimized using the technique for order of preference by similarity to ideal solution (TOPSIS) optimization technique. © 2022 K. G Sagar et al.},
note = {4},
keywords = {MECH},
pubstate = {published},
tppubtype = {article}
}
In this research work, an attempt was made to machine the titanium (Ti6Al4V) alloy utilizing electric discharge machining technique. The distinct process parameters and its impact on the machining performance were identified using the cause-and-effect diagram (CED). The key process parameters identified by CED diagram were current, pulse on time (Ton), aluminium oxide (Al2O3) powder concentration, and gap distance; experiments were conducted by varying the process parameters, experimental runs were designed using the Taguchi mixed orthogonal array. The experimental results revealed that improvement in material removal rate (MRR) was due to the bridging effect; reduction in tool wear rate (TWR) owing to the expansion of spark gap and enhancement in the surface roughness (Ra) was due to the complete flushing of machined debris. The interaction impact was analysed using the contour plot and with the aid of mathematical modelling experimental fits that were identified and the results were validated utilizing the sensitivity analysis. The obtained results were optimized using the technique for order of preference by similarity to ideal solution (TOPSIS) optimization technique. © 2022 K. G Sagar et al.
38.
Sagar, K. G.; Ramachandran, K.; Kosanam, K.; Bharathi, Marxim; Rama, M.; Bhattacharya, S.
Elsevier Ltd, vol. 69, 2022, ISBN: 22147853 (ISSN), (7).
@proceedings{105,
title = {Analysis of wear behavior and shear properties of nano – ZnO2/jute fiber/epoxy composites by Hand layup technique},
author = {K. G. Sagar and K. Ramachandran and K. Kosanam and Marxim Bharathi and M. Rama and S. Bhattacharya},
doi = {10.1016/j.matpr.2022.08.326},
isbn = {22147853 (ISSN)},
year = {2022},
date = {2022-01-01},
journal = {Materials Today: Proceedings},
volume = {69},
pages = {1274-1279,},
publisher = {Elsevier Ltd},
abstract = {Nano-ZnO2 was added to a jute fiber/epoxy composite whether it affected wear metrics and compressive strength. A silane-coupling agent with an amin-terminated end was used to functionalize the surface of ZnO2 nanoparticles. The modified ZnO2 was characterized employing FTIR spectroscopy and TGA. Using the hand layup method, the multiscale composites were created by distributing different amounts of nanoparticles (0.5, 1, 3, also 5 wt%) in matrix. The 3weight%ZnO2-filled compound has the finest characteristics out of all of these specimens. Adding ZnO2 to the composites caused in a 43 % increase in Compressive property in the short-beam shear test. Adding 3 wt. %ZnO lowered wear rate by 80 % and COF by 48 %. Wear and fracture mechanisms were studied. According to the discoveries of this study, M - ZnO2 is a promising alternative to be employed as a nanofiller in fiber-strengthened polymeric matrix. An epoxy/jute composite was supplemented with nano-ZnO2 to examine the effects on wear metrics and interlaminar shearing strength during dry sliding, according to the study's findings. A silane-coupling agent with an amin-terminated end was used to functionalize the surface of ZnO2 nanoparticles. The modified ZnO2 was characterized using Fourier-transform infrared spectroscopy and thermogravimetric studies. Hand layup was used to disperse different quantities of nanoparticles (0.5, 1, 3, also 5 wt%) in the matrix to create the multiscale composites. The 3 wt%ZnO2-filled composite has the finest characteristics out of all of these specimens. © 2022},
note = {7},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Nano-ZnO2 was added to a jute fiber/epoxy composite whether it affected wear metrics and compressive strength. A silane-coupling agent with an amin-terminated end was used to functionalize the surface of ZnO2 nanoparticles. The modified ZnO2 was characterized employing FTIR spectroscopy and TGA. Using the hand layup method, the multiscale composites were created by distributing different amounts of nanoparticles (0.5, 1, 3, also 5 wt%) in matrix. The 3weight%ZnO2-filled compound has the finest characteristics out of all of these specimens. Adding ZnO2 to the composites caused in a 43 % increase in Compressive property in the short-beam shear test. Adding 3 wt. %ZnO lowered wear rate by 80 % and COF by 48 %. Wear and fracture mechanisms were studied. According to the discoveries of this study, M - ZnO2 is a promising alternative to be employed as a nanofiller in fiber-strengthened polymeric matrix. An epoxy/jute composite was supplemented with nano-ZnO2 to examine the effects on wear metrics and interlaminar shearing strength during dry sliding, according to the study's findings. A silane-coupling agent with an amin-terminated end was used to functionalize the surface of ZnO2 nanoparticles. The modified ZnO2 was characterized using Fourier-transform infrared spectroscopy and thermogravimetric studies. Hand layup was used to disperse different quantities of nanoparticles (0.5, 1, 3, also 5 wt%) in the matrix to create the multiscale composites. The 3 wt%ZnO2-filled composite has the finest characteristics out of all of these specimens. © 2022
39.
Karthik, R.; Elangovan, K.; Girisha, K. G.
Tribological characterization of the LASER sintered and unsintered Inconel 718 in dry sliding condition Proceedings
Elsevier Ltd, vol. 47, 2021, ISBN: 22147853 (ISSN), (1).
@proceedings{160,
title = {Tribological characterization of the LASER sintered and unsintered Inconel 718 in dry sliding condition},
author = {R. Karthik and K. Elangovan and K. G. Girisha},
doi = {10.1016/j.matpr.2021.04.556},
isbn = {22147853 (ISSN)},
year = {2021},
date = {2021-01-01},
journal = {Materials Today: Proceedings},
volume = {47},
pages = {2486-2490,},
publisher = {Elsevier Ltd},
abstract = {Inconel 718 is a super alloy currently employed in aerospace, oil and gas industries, defence, jet engines etc., where it is used in the harsh working environment. Due to the excellent creep and fatigue behavior, the Inconel is most preferable in the above applications. Among the additive manufacturing processes Direct metal laser sintering (DMLS) is used to fabricate the Inconel components with high dimensional accuracy with less wastage of material. In the present work, dry sliding wear behavior of the laser sintered and conventionally manufactured Inconel 718 was investigated using pin on disc tribometer at room temperature as per ASTM G99 standard. Surface morphology of the worn samples were studied using scanning electron microscope. The presence of the nickel based element were confirmed using EDAX analysis. The results revels that, LASER sintered Inconel components shows the excellent wear resistance as compared to conventionally manufactured components. This is due to less porosity, high density and extreme surface micro hardness of the laser sintered components. © 2021 Elsevier Ltd. All rights reserved.},
note = {1},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Inconel 718 is a super alloy currently employed in aerospace, oil and gas industries, defence, jet engines etc., where it is used in the harsh working environment. Due to the excellent creep and fatigue behavior, the Inconel is most preferable in the above applications. Among the additive manufacturing processes Direct metal laser sintering (DMLS) is used to fabricate the Inconel components with high dimensional accuracy with less wastage of material. In the present work, dry sliding wear behavior of the laser sintered and conventionally manufactured Inconel 718 was investigated using pin on disc tribometer at room temperature as per ASTM G99 standard. Surface morphology of the worn samples were studied using scanning electron microscope. The presence of the nickel based element were confirmed using EDAX analysis. The results revels that, LASER sintered Inconel components shows the excellent wear resistance as compared to conventionally manufactured components. This is due to less porosity, high density and extreme surface micro hardness of the laser sintered components. © 2021 Elsevier Ltd. All rights reserved.
40.
Patil, V.; Janawade, S.; Kulkarni, S. N.; Biradar, A.
Elsevier Ltd, vol. 46, 2021, ISBN: 22147853 (ISSN), (5).
@proceedings{166,
title = {Studies on mechanical behavior and morphology of alumina fibers reinforced with aluminium-4.5% copper alloy metal matrix composites},
author = {V. Patil and S. Janawade and S. N. Kulkarni and A. Biradar},
doi = {10.1016/j.matpr.2020.06.176},
isbn = {22147853 (ISSN)},
year = {2021},
date = {2021-01-01},
journal = {Materials Today: Proceedings},
volume = {46},
pages = {99-106,},
publisher = {Elsevier Ltd},
abstract = {Present investigation aims at the development of Aluminium-4.5% Copper with Alumina fibers (Al2O3) material to form hybrid composite material through Squeeze Casting Process. Samples of 10, 20 and 30 wt% of Alumina Fibers in Aluminium-4.5% Copper matrix were prepared. The various tensile properties of the materials viz., ultimate tensile yield stress were evaluated utilizing a standard 40 ton Universal Tensile Machine of model UTM ZD the ultimate tensile strength tests were as per ASTM E8-82 guidelines. The tensile specimens of diameter 10 mm and gauge length 80 mm were machined from the cast specimens with the gauge length of the specimens parallel to the longitudinal axis of the castings. The ductility of the specimens was assessed in terms of percentage elongation and Yield stress. The corrosion test results are presented for both base material and with reinforced material. The corrosion rate decreased with the duration of immersion. Aluminum-4.5% copper and aluminum-4.5% copper with alumina fibers showed a weight loss intermediate between the two in the chosen corrosion medium. The hybrid composite (aluminum-4.5% copper with alumina fiber reinforcement) specimens showed better corrosion pitting resistance than the base specimens. Scanning electron microscopy analysis was used to study the morphology of the composite materials. © 2021 Elsevier Ltd. All rights reserved.},
note = {5},
keywords = {MECH},
pubstate = {published},
tppubtype = {proceedings}
}
Present investigation aims at the development of Aluminium-4.5% Copper with Alumina fibers (Al2O3) material to form hybrid composite material through Squeeze Casting Process. Samples of 10, 20 and 30 wt% of Alumina Fibers in Aluminium-4.5% Copper matrix were prepared. The various tensile properties of the materials viz., ultimate tensile yield stress were evaluated utilizing a standard 40 ton Universal Tensile Machine of model UTM ZD the ultimate tensile strength tests were as per ASTM E8-82 guidelines. The tensile specimens of diameter 10 mm and gauge length 80 mm were machined from the cast specimens with the gauge length of the specimens parallel to the longitudinal axis of the castings. The ductility of the specimens was assessed in terms of percentage elongation and Yield stress. The corrosion test results are presented for both base material and with reinforced material. The corrosion rate decreased with the duration of immersion. Aluminum-4.5% copper and aluminum-4.5% copper with alumina fibers showed a weight loss intermediate between the two in the chosen corrosion medium. The hybrid composite (aluminum-4.5% copper with alumina fiber reinforcement) specimens showed better corrosion pitting resistance than the base specimens. Scanning electron microscopy analysis was used to study the morphology of the composite materials. © 2021 Elsevier Ltd. All rights reserved.
