Regression and Grey relational analysis on friction and wear behavior of AA6061/Al2O3/Si3N4/graphite hybrid nano composites

Hariharasakthisudhan, P.; Moshi, A. Arul Marcel; Bharathi, S.; Logesh, K.

doi:10.1088/2053-1591/ab1cd3

Cited by 34 publications

(18 citation statements)

References 19 publications

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“…9 The premixing concept was further proved by the authors in some other studies. 8,32–34 Due to premixing, the metal powder skin of reinforcement enhanced its reactivity with matrix Al and dispersed them uniformly in the matrix. The improved mechanical properties were observed due to the effective load transfer between matrix and reinforcement as a result of premixing.…”

Section: Methodsmentioning

confidence: 99%

“…Normally, the laboratory test conditions of pin-on-disk wear test would be considered as the following: normal load: 10–40 N, sliding speed: 1–4 m/s, sliding distance: 1 km. 8,34 As the work focused on optimizing the process parameters of ultrasonication, the conditions of pin-on-disc wear test required to be fixed for all the experiments. The test conditions were arbitrarily fixed from the generally considered range of laboratory test conditions.…”

Section: Methodsmentioning

confidence: 99%

“…The input factors in wear test conditions usually considered for optimizing the wear characteristics are known to be normal load, sliding distance, 5 sliding speed, and percentage of reinforcement added. 6 The response factors for optimization of tribological and mechanical behavior of composites were noted as wear rate, 7 coefficient of friction, 8,9 hardness, 9 modulus of elasticity, 10 ultimate tensile strength, 11 etc. The optimal setting of input factors, to obtain desirable response factor values, are identified with the help of various models and multiresponse optimization tools.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal setting of input factors, to obtain desirable response factor values, are identified with the help of various models and multiresponse optimization tools. 8 Researchers have also proposed suitable statistical tools like regression models, neural network models, etc., to relate the input and output factors, which helped to predict the output values for the combination of input factors without performing actual experimentations. 8,12…”

Section: Introductionmentioning

confidence: 99%

“…8 Researchers have also proposed suitable statistical tools like regression models, neural network models, etc., to relate the input and output factors, which helped to predict the output values for the combination of input factors without performing actual experimentations. 8,12…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimizing the ultrasonication effect in stir-casting process of aluminum hybrid composite using desirability function approach and artificial neural network

Kamaraj

Hariharasakthisudhan²,

Moshi³

2021

Proceedings of the Institution of Mechanical Engineers, Part L:

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The ultrasonic-assisted stir-casting technique improves the uniform dispersion of nano-reinforcements in aluminum hybrid metal matrix composites. In the present study, the process parameters of the ultrasonic-assisted stir-casting method, such as ultrasonic vibration time, and depth of ultrasonic vibration along with the speed of mechanical stirrer, are optimized on A356 hybrid composite material optimally reinforced with aluminum nitride, multiwalled carbon nanotubes, graphite particles, and aluminum metal powder using the desirability function approach. The process parameters are optimized against the response factors such as porosity, ultimate tensile strength, and wear rate of the composites. The optimum combination of input factors is identified as stirring speed (600 r/min), ultrasonic vibration time (2 min), and depth of ultrasonic vibration (40 mm) among the selected range. The corresponding output response values are found to be porosity (1.4%), ultimate tensile strength (247 MPa), and wear rate (0.0013 mm3/min). The ANOVA results have revealed that depth of ultrasonic vibration showed significant contribution among the input factors. An artificial neural network model is developed and validated for the given set of experimental data.

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