TY  - JOUR
AU  - Stojanović, Blaža
AU  - Gajević, Sandra
AU  - Kostić, Nenad
AU  - Miladinović, Slavica
AU  - Vencl, Aleksandar
PY  - 2022
UR  - https://machinery.mas.bg.ac.rs/handle/123456789/3790
AB  - 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.
PB  - Emerald Group Publishing Ltd, Bingley
T2  - Industrial Lubrication and Tribology
T1  - Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods
EP  - 359
IS  - 3
SP  - 350
VL  - 74
DO  - 10.1108/ILT-07-2021-0262
ER  - 

@article{
author = "Stojanović, Blaža and Gajević, Sandra and Kostić, Nenad and Miladinović, Slavica and Vencl, Aleksandar",
year = "2022",
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.",
publisher = "Emerald Group Publishing Ltd, Bingley",
journal = "Industrial Lubrication and Tribology",
title = "Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods",
pages = "359-350",
number = "3",
volume = "74",
doi = "10.1108/ILT-07-2021-0262"
}

Stojanović, B., Gajević, S., Kostić, N., Miladinović, S.,& Vencl, A.. (2022). Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods. in Industrial Lubrication and Tribology
Emerald Group Publishing Ltd, Bingley., 74(3), 350-359.
https://doi.org/10.1108/ILT-07-2021-0262

Stojanović B, Gajević S, Kostić N, Miladinović S, Vencl A. Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods. in Industrial Lubrication and Tribology. 2022;74(3):350-359.
doi:10.1108/ILT-07-2021-0262 .

Stojanović, Blaža, Gajević, Sandra, Kostić, Nenad, Miladinović, Slavica, Vencl, Aleksandar, "Optimization of parameters that affect wear of A356/Al2O3 nanocomposites using RSM, ANN, GA and PSO methods" in Industrial Lubrication and Tribology, 74, no. 3 (2022):350-359,
https://doi.org/10.1108/ILT-07-2021-0262 . .