首页|Findings from College of Information Engineering Provide New Insights into Machine Learning (Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning)
Findings from College of Information Engineering Provide New Insights into Machine Learning (Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning)
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Research findings on artificial intelligence are discussed in a new report. According to news reporting from Lanzhou, People’s Republic of China, by NewsRx journalists, research stated, “Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology.” Funders for this research include Scientific Research Project of The Lanzhou Petrochemical University of Vocational Technology. Our news journalists obtained a quote from the research from College of Information Engineering: “In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient.”
College of Information EngineeringLanzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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