Machine learning-based prediction of friction torque and friction coefficient in statically loaded radial journal bearings

In this research, we utilized machine learning (ML) algorithms to predict the friction torque and friction coef-ficient in a statically loaded radial journal bearing. The study investigated the influence of temperature, bearing load, and rotational speed on the variation in friction torque and friction coefficient. Three different ML algo-rithms, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Regression Trees (RT), were applied to experimental tribological data. Performance assessment demonstrated that ML-based models can successfully predict the variation of friction torque and friction coefficient. Furthermore, we conducted a comparative analysis to evaluate the performance of ML-based models in relation to each other. The results of this study have useful implications for the design and optimization of statically loaded radial journal bearings.

Automated summary

In this research, machine learning algorithms were used to predict the variations of friction torque and friction coefficient in a statically loaded radial journal bearing. The effects of temperature, bearing load, and rotational speed on these variations were investigated. The results showed that machine learning models can successfully predict the variations of friction torque and friction coefficient. Additionally, a comparative analysis was conducted to evaluate the performance of different machine learning models. These findings have important implications for the design and optimization of statically loaded radial journal bearings.