To improve the tribology properties of the polymer coating under elevated temperature, the epoxy coating was reinforced with nano graphene. The micro-hardness, heat conductivity, and thermo-gravimetric properties of the coating were enhanced as filled graphene. The friction and wear properties of the polymer coating were studied using a pin-on-disc tribo-meter under room and elevated temperatures. The results showed that under room temperature, the friction coefficient and the wear rate of the coating adding 4.0 wt % graphene was 80% and 76% lower than that of the neat epoxy coating, respectively. As the test temperature increased, the friction coefficient of the graphene/polymer coatings decreased at first and then slightly increased. The friction coefficient was at its lowest value under 150 °C and then increased as the temperature rose to 200 °C. By adding 4.0 wt % graphene, the friction coefficient and wear rate of the polymer coating were further reduced, especially at elevated temperatures.

The function of the turbo-pump shaft within the liquid rocket engine is rendered exceedingly complex due to the operation in an environment characterized by drastically low temperatures, elevated velocities, and high pressures. Given these operational conditions, it is highly plausible that a two-phase flow might form within the liquid film located on the terminal face of the mechanical seal. This liquid-vapor mixture significantly modifies the fluid lubrication pattern across the end faces and poses consequential implications on the overall sealing stability. In this study, the phase change characteristics of the fluid in the seal clearance were investigated based on the Laminar and Mixture multi-phase flow models in a spiral groove mechanical seal. The behaviors of two-phase flow characteristics and phase transition under extreme temperature, rotational speed and pressure with liquid nitrogen media were studied. The sealing performance was quantified through metrics including the leakage rate, opening force, and internal gas phase volume ratio. The findings offered valuable insights into the role of operational conditions in influencing the phase change of the liquid film. Moreover, we discerned and explicated the intricate interconnections between the leakage rate, the opening force, and the phase change behavior of the liquid film.

Purpose: To meet the high stiffness requirement of bearings used in high-precision spindles, this paper aims to propose a novel kind of bearing composited by hydrostatic cavities and tilting pads with preload. Design/methodology/approach: Cavities are cut on the oil seal surface of a hybrid bearing, in which the tilting pads are set up. The load of the bearing is carried by the hydrostatic cavities and tilting pads. The structural features of this compound bearing and the controlling variables of the main stiffness coefficient are presented. Two basic design principles are proposed on the basis of equal machining clearance (EMC) and equal installation clearance (EIC). Findings: The theoretical analysis indicates that the stiffness of compound bearings under the EMC condition increases to infinity monotonously when the preload coefficient of the tilting pad tends to 1, while the stiffness under the EIC condition has a peak value. Therefore, a synthetic design principle is proposed by synthetically using the above-mentioned two principles. The applicable range of the three principles is discussed through an example. Originality/value: The study about technological combination of hydrostatic cavity and tilting pad in this paper can provide suggestions for the design of a high-stiffness bearing in a precision spindle.

The thermal error caused by the temperature rise in the service condition of the hydrostatic turntable system has a significant impact on the accuracy of the machine tool. The temperature rise is mainly caused by the friction heat of the bearing and the heat of the oil pump. The amount of heat mainly depends on the working parameters, such as the oil supply pressure and the oil film gap. The unreasonable parameter setting will cause the reduction in the internal flow of the hydrostatic bearing and the increase in the oil pump power, which makes the heat of the lubricating oil increase and the heat dissipation capacity decrease during the movement. Based on the established hydrostatic turntable system, in order to explore the main influencing factors of its thermal error, the temperature field model of the component is established by calculating the thermal balance of the key components of the system. The thermal coupling analysis of the component is carried out by using the model, and the temperature rise, deformation and strain curves of the hydrostatic turntable system under different service conditions are obtained. The results show that with the increase in the temperature, the deformation and strain of the bearing increase monotonously. For every 1 °C increase, the total deformation of the bearing increases by about 0.285 $\mathsf{\mu }\mathrm{m}$. The higher the oil supply pressure, the higher the temperature rise in the system. The larger the oil film gap, the lower the temperature rise in the system. The oil supply pressure has a greater influence on the temperature rise and thermal deformation than the oil film gap. This study provides a valuable reference for reducing the thermal error generated by the hydraulic turntable of the ultra-precision lathe.

Wear state prediction based on oil monitoring technology enables the early identification of potential wear and failure risks of friction pairs, facilitating optimized equipment maintenance and extended service life. However, the complexity of lubricating oil monitoring data often poses challenges in extracting discriminative features, limiting the accuracy of wear state prediction. To address this, a CNN–LSTM–Attention network is specially constructed for predicting wear state, which hierarchically integrates convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal dynamics modeling, and self-attention mechanisms for adaptive feature refinement. The proposed architecture implements a three-stage computational pipeline. Initially, the CNN performs hierarchical extraction of localized patterns from multi-sensor tribological signals. Subsequently, the self-attention mechanism conducts adaptive recalibration of feature saliency, prioritizing diagnostically critical feature channels. Ultimately, bidirectional LSTM establishes cross-cyclic temporal dependencies, enabling cascaded fully connected layers with Gaussian activation to generate probabilistic wear state estimations. Experimental results demonstrate that the proposed model not only achieves superior predictive accuracy but also exhibits robust stability, offering a reliable solution for condition monitoring and predictive maintenance in industrial applications.