Investigation of the Wear Behavior of AA6063/Zirconium Oxide Nanocomposites Using Hybrid Machine Learning Algorithms

Abstract

This research created hot-pressed composites of the AA6063 matrix with varying concentrations of ZrO2 (0.25, 0.5, and 1 wt %). At sliding speeds of 80, 120, and 150 mm/s, the wear performance of the specimen was studied at loads of 10 N, 15 N, 20 N, and 25 N. The authors analyzed the counter-face material, the wear debris, and the worn surfaces to learn about the wear mechanisms. Developing these three machine learning (ML) algorithms was to evaluate the ability to predict wear behavior using the same small dataset collected using varying test processes. A thorough examination of each model hyperparameter tuning phase was performed. The predictive performance was analyzed using several statistical tools. The most effective decision-making algorithms for this data collection were those based on trees. Predictions made by the decision tree algorithm for the test and validation measurements have an accuracy of 86% and 99.7%, respectively. The best model was picked out based on the results of the predictions.