first_pagesettingsOrder Article Reprints Open AccessArticle Prediction of Tribological Properties of Alumina-Coated, Silver-Reinforced Copper Nanocomposites Using Long Short-Term Model Combined with Golden Jackal Optimization by Ismail R. Najjar 1,Ayman M. Sadoun 1,Adel Fathy 2,3ORCID,Ahmed W. Abdallah 2,Mohamed Abd Elaziz 4,5,6ORCID andMarwa Elmahdy 3,* 1 Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah P.O. Box 80204, Saudi Arabia 2 Department of Mechanical Design and Production Engineering, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt 3 Mechanical Department, Higher Technological Institute, Tenth of Ramadan City 44629, Egypt 4 Faculty of Computer Science & Engineering, Galala University, Suze 43511, Egypt 5 Artificial Intelligence Science Program, Faculty of Science & Engineering, Galala University, Suze 43511, Egypt 6 Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt * Author to whom correspondence should be addressed. Lubricants 2022, 10(11), 277; https://doi.org/10.3390/lubricants10110277 Received: 28 September 2022 / Revised: 19 October 2022 / Accepted: 20 October 2022 / Published: 24 October 2022 (This article belongs to the Special Issue Tribological Applications of Nano & Submicro Structured Materials) Download Browse Figures Versions Notes Abstract 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.