Prediction of grease performance and optimal additive ratio based on the SSA-GDA-LSSVM model

In this paper, to address the issue of compounding three additives in PTFE grease, we propose a machine learning model based on SSA-GDA-LSSVM to predict both the tribological performance and the optimal ratio of additives in PTFE grease. Gaussian data augmentation expanded the experimental data, and the Sparrow Algorithm optimized hyperparameters of the Least Squares Support Vector Machine. SHAP analysis clarified model predictions, and a Non-Dominated Sorting Genetic Algorithm identified optimal additive ratios, which were experimentally validated. The results showed that the model predicted friction coefficients and wear scar widths with R² values exceeding 0.97, and the experimental error for optimal ratios was less than 1%. • A novel SSA-GDA-LSSVM model was developed for predicting lubricating grease performance. • High-accuracy predictions of friction coefficient and wear scar width were achieved through data augmentation and hyperparameter optimization. • Multi-objective optimization identified the optimal additive ratios for enhanced tribological properties.