Fabrication and Dry-Sliding Wear Characterization of Open-Cell AlSn6Cu–Al2O3 Composites with LSTM-Based Coefficient of Friction Prediction

Abstract

This study investigates the fabrication, wear characterization, and coefficient of friction (COF) prediction of open-cell AlSn6Cu–Al2O3 composites obtained by a liquid-state processing technique. Focusing on wear behavior under varying loads using the pin-on-disk method, this research characterizes microstructure and phase composition via SEM, EDS, and XRD analyses. A novel aspect of this research is the application of an LSTM recurrent neural network model for the fast and accurate prediction of the COF of the composites, eliminating the need for extensive experimental work. Additionally, feature importance analysis using Random Forest regressors is conducted to ascertain the relative contribution of each input variable to the output variable, enhancing our understanding of the wear mechanisms in these materials. The results demonstrate the effectiveness of the composite’s reinforcement in improving wear resistance, highlighting the critical role of mechanical stress and the reinforcement’s hardness in the wear process. The quantitative findings related to the wear behavior include a mass-wear reduction in the open-cell AlSn6Cu–Al2O3 composite from 8.05 mg to 1.90 mg at 50 N and a decrease from 17.55 mg to 8.10 mg at 100 N, demonstrating the Al2O3 particles’ effectiveness in improving wear resistance under different loads.