Wear Behavior Prediction for Cu/TiO2 Nanocomposite Based on Optimal Regression Methods

Abstract

The present study investigated the effects of the addition of the TiO2 nanoparticles with different weight percent on the copper nanocomposites' abrasive wear behavior. In addition, optimal machine learning regression (OMLR) methods are used to detect the copper nanocomposites' abrasive wear behavior. The powder metallurgy method is used to fabricate the Cu/TiO2 nanocomposite specimens with 0, 4, 8, 12 wt% TiO2. The abrasive wear behavior of fabricated specimens is evaluated experimentally using a pin on the desk apparatus. The abrasive wear results are used to predict the abrasive wear behavior of the fabricated composites using OMLR methods. OMLR methods are implemented and carried out using MATLAB/software. The OMLR methods use the input parameters of TiO2, sliding distance and load, and the weight loss due to abrasive wear as an output to build their optimal models. OMLR methods were successfully detected with small errors, especially GPR methods. The results of the proposed GPR were compared with those obtained from the ANN model with the efficacy of the GPR model. The experimental results demonstrated that the weight loss in test specimens decreased with increasing wt% of TiO2 addition. This reflected improvements in the wear resistance of copper nanocomposites compared to pure copper.