T.S. Sachit

Symbiosis International University

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Arunkumar Bongale

Symbiosis International University

N. Mohan

Mathrusri Ramabai Ambedkar Dental College & Hospital

Nitin Khedkar

Symbiosis International University

G.S. Pradeep Kumar

Christ University

M. AkshayPrasad

International Institute of Information Technology Bangalore

Raghu Yogaraju

Bangalore Diabetes Centre

Priya Jadhav

Symbiosis International University

D. Vinay Kumar

Vignan's Foundation for Science, Technology & Research

R. Keshavamurthy

Dr. Hari Singh Gour University

R. Suresh

M S Ramaiah University of Applied Sciences

Cooperative Institutions

Symbiosis International University

Christ University

Visvesvaraya Technological University

Dr. Hari Singh Gour University

International Institute of Information Technology Bangalore

National Aerospace Laboratories

National Institute of Technology Tiruchirappalli

Mathrusri Ramabai Ambedkar Dental College & Hospital

Ambedkar University Delhi

M S Ramaiah University of Applied Sciences

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Thermal analysis of Cr2O3 coated diesel engine piston using FEA

Materials Today Proceedings (2018)

T.S. Sachit R.V. Nandish Mallikarjun

Powder Metallurgy

Metal matrix composite

Piston (optics)

Volume fraction

10.1016/j.matpr.2017.12.086

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Citations (15)

Evaluation of Surface Delamination in Drilled Fiber-Reinforced Polymer Composites Using Digital Image Processing

Journal of The Institution of Engineers (India) Series D (2023)

Nanjangud Mohan C. Hemanth Kumar T.S. Sachit Arunkumar Bongale

Delamination

10.1007/s40033-023-00464-z

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Citations (4)

Tribological and mechanical investigation of coir-silk reinforced polypropylene based matrix composites.

IOP Conference Series Materials Science and Engineering (2021)

D. Vinay Kumar C. Nanjundaswamy T.S. Sachit Nanjangud Mohan

Abstract Silane treated natural fibers such as silk, coconut coir is reinforced into the polypropylene (PP) with a varying weight fraction of fibers and matrixes are developed in twin screw extruder at a constant temperature followed by the hot compression molding. Totally three different weight percentage of the polymer composites are developed. Such as silk fiber reinforced composites (S-PP), coir fiber reinforced composites (C-PP) and silk along with coir fiber reinforced composites (S-C-PP). The developed composites are then subjected to various tests. The water absorption capabilities of the all three composites are investigated. The composites are tending to abrasive wear test as per the ASTM standards. Design of experiments L-9 orthogonal array was introduced to identify the optimal wear condition of the composites developed. From the results it is observed that the tricholo vinyl silane chemical treatment to the coir and silk fibers enhances the tribo-mechanical properties of the fiber reinforced polymer composites. Also, the wear life of the composites was increased by 35% and water absorption capability increased by 42%. The scanning electron microscopy helped in identifying the impact of wear test over the surface of the composites at higher magnifications. The detailed surface morphology was executed in order to identify the behavior of the composites upon application of load.

Coir

Polypropylene

Compression molding

Natural fiber

10.1088/1757-899x/1091/1/012052

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Citations (0)

Effect of Particle Size on Mechanical and Tribological Behavior of LM4/SiCp Based MMC

Materials Today Proceedings (2018)

T.S. Sachit Naresh Prasad Mohammed Aameer Khan

Particle (ecology)

10.1016/j.matpr.2017.12.189

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Citations (21)

Studies on mechanical and dry sliding wear behaviour of graphite/flyash reinforced aluminium (Al6Mg) MMCs

Materials Today Proceedings (2019)

K. Rajesh K V Mahendra N. Mohan T.S. Sachit M. AkshayPrasad

Brinell scale

Tensile testing

10.1016/j.matpr.2019.09.211

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Citations (9)

Wear rate optimization of tungsten carbide (WC) nano particles reinforced aluminum LM4 alloy composites using Taguchi techniques

Materials Research Express (2019)

T.S. Sachit N. Mohan

The present research investigation focus on wear behavior of tungsten carbide reinforced aluminum LM4 alloy composites. Powder metallurgy technique was incorporated to develop the composite specimens by varying the reinforcement percentage of WC particles from 0.5 wt% to 2 wt% in step of 0.5 wt%. Scanning Electron Microscopy analysis and Energy Dispersion Spectroscopy examination carried out on the powder particles as well as on the developed specimens. Pin-on-disc wear testing machine utilized to evaluate the wear rate. Taguchi design of experiment was incorporated to understand the influence of process parameters on dry sliding wear behavior. Optimization of process parameters like applied load, sliding speed and particle reinforcement percentage was done using S/N ratio and ANOVA method. 'Smaller is better' characteristic selected for dry sliding wear analysis. Results revealed that applied load (72.56%) shows significant impact on wear rate followed by sliding speed (15.28%) and percentage of reinforcement (8.90%). The confidence interval predicted for a confidence level of 95%. The predicted optimal values of wear rate are within the specified range, and it validated with a confirmation experiment. SEM analysis was done on the worn out surfaces to understand the wear mechanisms and found that more delamination occurs during an increase in applied load.

Tungsten Carbide

Delamination

Powder Metallurgy

10.1088/2053-1591/ab0e3f

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Citations (12)

Wear performance analysis of B₄C and graphene particles reinforced Al–Cu alloy based composites using Taguchi method

Journal of the Mechanical Behavior of Materials (2023)

T.S. Sachit Arunkumar Bongale Satish Kumar Priya Jadhav

Abstract In this study, the wear performance of boron carbide (B 4 C) and graphene (Gr) particles reinforced Al–Cu alloy composites was investigated. The composite samples were made using the solid-state manufacturing process. The wear performance was assessed using a pin-on-disc tribometer. The Taguchi optimization approach was used to determine the performance of each parameter. All experiments were carried out using the L27 array, which included three sets of parameters such as applied load, disc speed, and reinforcement percentage. The ANOVA approach was used to examine the impact of each parameter. According to the findings, the weight on the pin has the greatest influence on wear, followed by sliding speed and reinforcing percentage. The addition of B 4 C particles improves the wear resistance, and the Gr functions as a self-lubricating agent while in use. Scanning electron microscope analysis of worn-out samples revealed an abrasive type of wear process.

Tribometer

Boron carbide

Orthogonal array

10.1515/jmbm-2022-0274

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Citations (2)

Synthesis and mechanical characterisation of aluminium-based hybrid nanocomposites reinforced with nano tungsten carbide and nano tantalum niobium carbide particles

International Journal of Materials Engineering Innovation (2018)

T.S. Sachit Mohan Nagaraj Arunkumar Bongale Nitin Khedkar

Metal matrix nanocomposites are potential light weight engineering materials. Nano-sized reinforcements enhance the properties of aluminium matrix compared to micro-sized reinforcements. Hybrid nano reinforcement exhibit superior properties to Al matrix composites as compared with Al matrix with single reinforcement. Aluminium LM alloys give useful matrix properties as compared with other Al alloy. The present investigation aims to synthesis and characterises the aluminium LM4 alloy matrix reinforced with nano tungsten carbide (WC) and nano tantalum niobium carbide (Ta/NbC) particles. The composites were prepared by powder metallurgy technique followed by compaction and sintering process. The reinforcement ratio varies from 0.5, 1, 1.5 and 2 wt% of WC and Ta/NbC particles. The micro-structural observations were carried out using X-ray diffraction (XRD) analysis and scanning electron microscope (SEM). Mechanical properties such as micro-hardness and density of the composites were investigated. On comparison with base alloy, results indicate that increasing weight percentage of reinforcement results in an improvement of 65.4% and 79.2% in micro-hardness for single and hybrid reinforcement composites respectively. Also, it has been observed that an increase in density of the composites as compared with base alloy.

Tantalum carbide

Niobium carbide

Powder Metallurgy

Tungsten Carbide

10.1504/ijmatei.2018.097299

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Citations (8)

Synthesis and mechanical characterization of aluminum based hybrid nano composites reinforced with nano tungsten carbide and nano tantalum niobium carbide particles

International Journal of Materials Engineering Innovation (2018)

Mohan Nagaraj Nitin Khedkar Arunkumar Bongale T.S. Sachit

Tantalum carbide

Powder Metallurgy

Niobium carbide

Tungsten Carbide

10.1504/ijmatei.2018.10017373

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Citations (3)

Wear rate optimization of Al/SiCnp/e-glass fibre hybrid metal matrix composites using Taguchi method and genetic algorithm and development of wear model using artificial neural networks

Materials Research Express (2018)

Arunkumar Bongale Satish Kumar T.S. Sachit Priya Jadhav

Studies on wear properties of Aluminium based hybrid nano composite materials, processed through powder metallurgy technique, are reported in the present study. Silicon Carbide nano particles and E-glass fibre are reinforced in pure aluminium matrix to fabricate hybrid nano composite material samples. Pin-on-Disc wear testing equipment is used to evaluate dry sliding wear properties of the composite samples. The tests were conducted following the Taguchi's Design of Experiments method. Signal-to-Noise ratio analysis and Analysis of Variance are carried out on the test data to find out the influence of test parameters on the wear rate. Scanning Electron Microscopic analysis and Energy Dispersive x-ray analysis are conducted on the worn surfaces to find out the wear mechanisms responsible for wear of the composites. Multiple linear regression analysis and Genetic Algorithm techniques are employed for optimization of wear test parameters to yield minimum wear of the composite samples. Finally, a wear model is built by the application of Artificial Neural Networks to predict the wear rate of the composite material, under different testing conditions. The predicted values of wear rate are found to be very close to the experimental values with a deviation in the range of 0.15% to 8.09%.

Orthogonal array

Metal matrix composite

Powder Metallurgy

10.1088/2053-1591/aab071

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Citations (23)