Dry sliding tribological characteristics evaluation and prediction of TiB2-CDA/Al6061 hybrid composites exercising machine learning methods

This study presents the fabrication and comprehensive tribological assessment of Al6061-based hybrid composites reinforced with Titanium diboride (TiB₂) and cow dung ash (CDA) using the stir casting technique. The wear behavior of TiB₂-CDA/Al6061 composites was systematically analyzed under dry sliding conditions utilizing a pin-on-disc setup. The study investigates the effects of key parameters, including reinforcement percentage (R), applied load (L), sliding velocity (V), and sliding distance (D), on wear loss and the coefficient of friction (COF) through a full factorial experimental design. Additionally, scanning electron microscopy (SEM) was employed to examine dominant wear mechanisms under extreme wear conditions, revealing adhesion, abrasion, oxidation, and delamination as primary degradation processes. Furthermore, machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Gaussian Process Regression (GPR), and Gradient Boosted Trees (GBTA), were leveraged to develop predictive models for wear loss and COF. The models were trained and validated using experimental data, demonstrating the efficacy of machine learning in accurately predicting tribological performance while minimizing extensive experimental trials. Among the models, GPR exhibited the highest predictive accuracy, surpassing other algorithms in forecasting wear behaviour.