Experimental investigation of wear behavior in Al2O3-reinforced glass fiber composites and comparative analysis of artificial neural network and machine learning models

This study experimentally investigates the effects of adding different amounts (1-5 wt.%) of Al2O3 particles on the wear behavior of glass fiber-reinforced epoxy composites to improve their tribological performance. Composite laminates produced using the hand-lay up method were subjected to wear tests using a ball-on-disc test setup under dry sliding conditions. Among all tested compositions, the composite containing 3 wt.% Al2O3 exhibited the highest wear resistance. Compared to the neat composite, the specific wear rate was reduced by up to 70%. In contrast, 4% and 5% Al2O3 additions resulted in a decrease in wear resistance due to particle agglomeration. While the highest specific wear rate was 260×10⁻⁶ mm³/Nm, this value decreased to 80×10⁻⁶ mm³/Nm in the 3% added sample. Furthermore, wear rate predictions were performed using models such as artificial neural network and different machine learning regressors. Random Forest (17.62%), Ridge regressor (18.46) and artificial neural network (19.92%) achieved the lowest MAPE values, indicating strong predictive performance for Al2O3-reinforced glass fiber composites. The artificial neural network model optimized with grid search achieved a mean squared error of 0.90 and a coefficient of determination of 0.92, while the random forest regressor demonstrated strong generalization with a coefficient of determination of 0.91. The results demonstrated the critical roles of both particle ratio and data-driven models in wear performance analysis.