Prediction of coefficient of friction of solid powder lubricants under high pressure conditions using machine learning algorithms Vorhersage des Reibungskoeffizienten von Festpulverschmierstoffen unter Hochdruckbedingungen mit Hilfe von Algorithmen des maschinellen Lernens

Conventional liquid lubricants prove inadequate for effective lubrication in conditions characterized by high temperatures and high vacuum environments. In such extreme scenarios, powder lubricants emerge as a more viable solution. The present study is to conduct a series of experiments using a reciprocating wear test setup and evaluate the capability of four different machine learning models in analysing the tribological attributes of metals when lubricated with three distinct powder types: zirconium dioxide, copper oxide, and molybdenum disulfide, specifically under conditions of elevated contact pressures and dry environments. The experiments were systematically carried out encompassing a range of load and temperature combinations. Four different machine learning models (MLP, KNN, extreme gradient boosting and light gradient-boosting machine) were used for predicting the coefficient of friction of metals lubricated with different powders. Extreme gradient boosting machine learning model gives better result than the other models with mean absolute error, root mean squared error, R² value and average absolute deviation percentage of 0.0215, 0.0278, 0.9962 and respectively.