Enhanced random vector functional link based on artificial protozoa optimizer to predict wear characteristics of Cu-ZrO2 nanocomposites

Owing to the absence of scientific methods for predicting nanocomposites' wear rates, a freshly updated machine learning method that uses an Artificial Protozoa Optimizer (APO) to forecast the tribological performance of CuZrO2 nanocomposites was proposed. The updated model was used to predict the coefficients of friction and wear rates of Cu-ZrO2 nanocomposites produced in this work. Copper-zirconia nanocomposite powders were fabricated utilizing the ball milling process, varying in milling time and ZrO2 weight percentage. The effect of reinforcement percentage and milling time on the morphology and microstructure of the copper composite powders was characterized. The study concentrated on the effects of high-energy ball milling on the morphology, microstructure, and microhardness of the resulting composites. After cold compaction at 700 MPa of pressure, the resultant powders were sintered for 2 h at 950 degrees C in a hydrogen atmosphere. Based on the results, a 20-h milling time is the best option for creating a Cu-ZrO2 nanocomposite with evenly distributed reinforcement. The microhardness and wear rate of the Cu-15%ZrO2 nanocomposites are improved by 66.2 %, and 81.1 %, respectively, when compared to pure copper. The crystallite size decreases significantly with the addition of ZrO2, reaching 32.5 and 11.1 nm for samples containing 5 and 15 % weight percent ZrO2. That is why there is a rise in wear and mechanical properties. For Cu-ZrO2 nanocomposites with reinforcement content up to 15 %, the model constructed using the APO method demonstrated excellent forecasting of the wear rate and coefficient of friction.