Abstract
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.
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