Work

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

Year: 2024

Type: article

Abstract: 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 %.

Source: Tribology International

DOI: 10.1016/j.triboint.2024.110366

Authors: Yanqiu Xia, Hanbin Zhao, Xin Feng

Language: English

Topic: Lubricants and Their Additives

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,230

Funders: Natural Science Foundation of Beijing Municipality, State Key Laboratory of Solid Lubrication

Awards: G3346299698, G447029714