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Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods

Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and... This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al2O3) nanoparticles.Design/methodology/approachMetal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al2O3 particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.FindingsThe combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al2O3, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.Originality/valueThe specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods

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References (24)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0036-8792
eISSN
0036-8792
DOI
10.1108/ilt-07-2021-0262
Publisher site
See Article on Publisher Site

Abstract

This study aims to present a novel methodology for the evaluation of tribological properties of new nanocomposites with the A356 alloy matrix reinforced with aluminium oxide (Al2O3) nanoparticles.Design/methodology/approachMetal matrix nanocomposites (MMnCs) with varying amounts and sizes of Al2O3 particles were produced using a compocasting process. The influence of four factors, with different levels, on the wear rate, was analysed with the help of the design of experiments (DoE). A regression model was developed by using the response surface methodology (RSM) to establish a relationship between the observed factors and the wear rate. An artificial neural network was also applied to predict the value of wear rate. Adequacy of models was compared with experimental values. The extreme values of wear rate were determined with a genetic algorithm and particle swarm optimization using the RSM model.FindingsThe combination of optimization methods determined the values of the factors which provide the highest wear resistance, namely, reinforcement content of 0.44 wt.% Al2O3, sliding speed of 1 m/s, normal load of 100 N and particle size of 100 nm. Used methods proved as effective tools for modelling and predicting of the behaviour of aluminium matrix nanocomposites.Originality/valueThe specific combinations of the optimization methods has not been applied up to now in the investigation of MMnCs. In addition, using of small content of ceramic nanoparticles as reinforcement has been poorly investigated. It can be stated that the presented approach for testing and prediction of the wear rate of nanocomposites is a very good base for their future research.

Journal

Industrial Lubrication and TribologyEmerald Publishing

Published: Mar 29, 2022

Keywords: A356; Nanocomposite; Wear; Design of experiments (DoE); Response surface methodology (RSM); Artificial neural network (ANN); Particle swarm optimization (PSO); Genetic algorithm (GA)

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