Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization

In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu-Al2O3 nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu-Al2O3 nanocomposites that were developed in this study. Electroless coating of Al2O3 nanoparticles with Ag was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical, and wear properties of the produced composites with different Al2O3 content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al2O3 content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al2O3, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites.

Automated summary

This study presented a newly modified machine learning model to predict the tribological performance of Cu-Al2O3 nanocomposites, which were developed using electroless coating of Al2O3 nanoparticles with Ag, ball milling, and compaction. It was found that adding 10% Al2O3 significantly enhanced the hardness and wear rates of the composites. The developed LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all considered composites.