| description abstract | The study focused on developing Al5052 composites reinforced with cenosphere particles to improve their wear resistance. The wear-rates of the test materials were measured using a pin-on-disc apparatus at room temperature, utilizing a dataset comprising 27 experimental observations. The results demonstrate that increasing the cenosphere reinforcement content effectively reduced the wear-rates. The microhardness improved from 68.5 Hv to 78.75 Hv by adding 4 wt% cenosphere particles to the Al5052 alloy. Four machine learning models—decision tree (DT), random forest (RF), support vector regression (SVR), and k-nearest neighbors (KNN)—were employed for wear-rate prediction. While the DT model achieved the highest test accuracy (R2 = 0.95), it exhibited signs of overfitting as indicated by its R2 of 1.0 on the training data. In contrast, the RF (R2 = 0.94) model provided a better balance between accuracy and generalizability, making it a more reliable choice for predictive analysis. An analysis of the importance of features was carried out to evaluate the contribution of input parameters to predict wear-rate. The results revealed that the reinforcement wt% had the most significant impact on wear-rate prediction. These findings suggest that data-driven machine learning approaches hold potential as powerful tools in tribological studies, paving the way for the emergence of tribo-informatics. | |
