Triboinformatic modeling of wear and friction coefficient of microwave-assisted synthesized g-C3N4/MoS2 nanocomposites using advanced regression models

Abstract Tribological phenomena, encompassing friction, wear, and lubrication, significantly impact the performance and efficiency of mechanical systems across various industries. This research investigates the application of machine learning approaches to minimize wear depth and coefficient of friction in tribometer systems by modeling the effects of applied load, sliding speed, and coating material. Through comprehensive experimentation and analysis, the influence of these critical parameters on the tribological responses is quantified. Several machine learning algorithms, including linear regression, decision trees, random forests, support vector regression, k-nearest neighbors, and neural networks, are employed to capture the complex relationships between the input parameters and the responses. The neural network model achieved the best performance with a low mean squared error (MSE) of 0.0023 and high R-squared of 0.9977 for predicting wear depth, along with an MSE of 567.89 and R-squared of 0.9654 for coefficient of friction predictions. Random forests also exhibited strong performance with testing MSE of 0.0298 and R-squared of 0.9671 for wear depth. Feature importance analysis identifies coating material as the most influential factor for wear depth, while sliding speed is the dominant factor (F-ratio of 8.589) affecting the coefficient of friction. Statistical significance testing confirms a substantial difference between the means (t-statistic of -14.544, p-value of 5.41e-31). Multiple linear regression analysis reveals an optimized input parameter combination of 10 kN applied load, 0.5 m/s sliding speed, and 9 Wt% of gCN, minimizing both tribological responses with low MSEs of 0.0214 (wear depth) and 12.8745 (coefficient of friction) on the training set. The study provides valuable insights into tribological optimization, highlighting the potential of machine learning techniques for enhancing system performance, efficiency, and durability. The findings contribute to a deeper understanding of tribological processes and pave the way for future research in developing advanced coatings, lubricants, and condition monitoring systems for tribological applications,