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

Abstract

Purpose

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/approach

Metal 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.

Findings

The 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/value

The 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.

Citation

Stojanović, B., Gajević, S., Kostić, N., Miladinović, S. and Vencl, A. (2022), "Optimization of parameters that affect wear of A356/Al₂O₃ nanocomposites using RSM, ANN, GA and PSO methods", Industrial Lubrication and Tribology, Vol. 74 No. 3, pp. 350-359. https://doi.org/10.1108/ILT-07-2021-0262