Tribo-Informatics Approach to Predict Wear and Friction Coefficient of Mg/Si3N4 Composites Using Machine Learning Techniques

Abstract

The demand for lightweight, high-performance materials has driven significant advancements in magnesium-based materials. However, their practical implementation faces challenges, primarily due to their low wear resistance, especially in industries like automotive, where weight reduction is vital for fuel efficiency. Furthermore, the process of fabricating and assessing wear behavior incurs time and cost constraints. In light of this, machine learning techniques have emerged as a crucial tool for predicting the mechanical properties, wear characteristics, and tribological performance of diverse materials, including magnesium and its composites. Accordingly, the present study aims to integrate experimental results with machine learning techniques. The objective is to predict the wear rate and friction coefficient of Mg/Si3N4 nanocomposites, thus optimizing material design and manufacturing for superior wear performance. Nanocomposites are fabricated through ultrasonic-assisted stir casting, and a dataset of 120 data points is collected using a pin-on-disc tribometer under dry sliding conditions. Five supervised machine learning regression models are employed to predict wear rate and coefficient of friction, with hyperparameter tuning for a fair comparison. Results are evaluated using various statistical metrics, identifying the most effective model for accurate wear behavior prediction. The study also demonstrates improved wear resistance and lower friction coefficients in nanocomposites compared to pure magnesium. This is attributed to the even distribution of Si3N4 nanoparticles and strong interfacial bonding with the matrix. The presence of a mechanically mixed layer further enhances wear resistance under high loads and speeds. Five wear modes are identified, including abrasion, oxidation, adhesion, delamination, and plastic deformation, providing valuable insights into the wear mechanisms. A comprehensive wear map facilitates a deeper understanding of wear behavior.