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Tribology Letters, 32
Information Sciences, 273
Materials Today: Proceedings, 5
Transactions of the Indian Institute of Metals, 71
Surface Engineering and Applied Electrochemistry, 54
Renewable and Sustainable Energy Reviews, 90
Composites Part B: Engineering, 52
IOP Conference Series: Materials Science and Engineering, 659
Journal of Mechanical Science and Technology, 30
Materials Science and Technology, 29
Applied Mathematics and Computation, 246
FME Transactions, 45
Manufacturing Review, 2
Journal of the Brazilian Society of Mechanical Sciences and Engineering, 41
IOP Conference Series: Materials Science and Engineering, 390
Powder Technology, 228
Transactions of the Canadian Society for Mechanical Engineering, 40
Arabian Journal for Science and Engineering, 42
Composites Part B: Engineering, 54
Engineering Applications of Artificial Intelligence, 27
Composites Part B: Engineering, 127
Swarm and Evolutionary Computation, 15
Arabian Journal for Science and Engineering, 40
Measurement, 94
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.
Industrial Lubrication and Tribology – Emerald 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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