润滑科技信息平台

Tribological properties and wear prediction of various ceramic friction pairs under seawater lubrication condition of different medium characteristics using CNN-LSTM method

出版日期: 2023-09-09

DOI: https://doi.org/10.1016/j.triboint.2023.108935

作者: Fanglong Yin; Zhuangzhuang He; Songlin Nie; Hui Ji; Zhonghai Ma;

The tribological performance and wear mechanism of polyetheretherketone (PEEK)/17–4PH stainless steel, PEEK/silicon carbide (SiC), WC-6Ni (YN6X)/SiC and SiC/polycrystalline diamond composite (PCD) tribopairs sliding in seawater at different temperatures (ranged from 25 °C to 70 °C) and salinities (20‰, 35‰ and 50‰) was investigated. A deep neural network model was used to predict the coefficient of friction, combining a one-dimensional CNN and an LSTM. The experiment results showed that increasing salinity led to a decrease in tribological performance of the tribopairs, while the performance effectively improved within the temperature range of 25–55 °C. The CNN-LSTM model demonstrated high accuracy in predicting results, which is significant for analyzing the tribological characteristics of tribopairs in seawater hydraulic components.

全文链接: https://www.sciencedirect.com/science/article/pii/S0301679X23007235/pdfft?md5=bd90e30b680f07709f583339ebb58de4&pid=1-s2.0-S0301679X23007235-main.pdf

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