Search

The tribological properties of matching pairs at different temperature domain is predicted through friction sound. Two deep learning algorithms respectively by virtue of the Nonlinear Auto-Regressive models with Exogenous Inputs and long short-term memory neural network are adopted to analyze the acoustic features of friction sound to predict tribological properties of polymer surface at five temperatures within a large temperature domain under different working conditions. The deep learning algorithm precisely fits the friction coefficient in accordance with the performance analysis. [Display omitted] • A real-time friction coefficient prediction method based on friction sound. • The mapping relationship between friction sound and friction coefficient with the same random nonlinear characteristics. • The prediction method was widely applicable to the prediction of friction coefficient under wide temperature range. • This method can be used for in-situ monitoring on tribological properties of mating pairs. [ABSTRACT FROM AUTHOR]

Two polymers and five metal pairs were tested at six low temperature points and three constant load speeds. The tribological properties were determined by joint iterative coupling of the temperature, load and friction velocity value. Molecular dynamics simulation was used to study the micro-tribological properties of friction pairs at the atomic scale. The macro-tribological performance test results were similar to the results of the molecular dynamics simulation and exhibited the same trend. Two profound hypothermia points were predicted. The evolution of the tribological mechanism and properties of the polymer interface under different working conditions at low temperature were revealed on the macroscopic and atomic scales. [Display omitted] • A friction mechanism researching method based on a combination of macro and micro research was developed. • The macroscopic and microscopic tribological evolution rules of a polymer interface at low temperature were found. • The simulation was applicable to predicting polymer materials' tribological properties and explaining the test phenomena. • This method can be used for researches under harsher conditions and more extreme environments. [ABSTRACT FROM AUTHOR]

A signal processing method based on friction noise is introduced to predict the tribological properties of polymers in a wide temperature domain. Three different machine learning algorithms, XGBoost, LightGBM and CatBoost are used to establish a mapping relationship between the time–frequency domain features of friction noise and the friction coefficient. The friction coefficients of pairs of four polymers and seven metals are predicted at five temperatures and three load/speed conditions of point contacts in a wide temperature domain from − 120–25 ℃. Performance analysis reveals that the machine learning approach satisfactorily predicts the friction coefficients of different polymer–metal pairs in a wide temperature range from tribological test data. This method can be used for in-situ monitoring on tribological properties. [Display omitted] • A real-time friction coefficient prediction method based on friction noise was developed by using machine learning algorithm. • The following mapping relationship between friction noise and friction coefficient was found. • The prediction method was widely applicable to the prediction of friction coefficient. • This method can be used for in-situ monitoring on tribological properties of mating pairs. [ABSTRACT FROM AUTHOR]

[Display omitted] • The failure reasons of reciprocating seal were analyzed by comprehensive tests. • Eight types of failure reasons and improvement measures were proposed. • Key points in seal design and assembly were put forward. • The failure analysis method makes the development of future seal products more effective. Hydraulic actuators are an important executive component of modern aircraft and are typically characterized by a reciprocating sealing form. Sealing failure affects the implementation of aircraft missions and flight safety. To determine the main cause for sealing failure of the ACGT-HP combined rod seal in an aircraft actuator, taking the new seal as the control group, compare and analyze the failure and non-failure combined rod seals under the same working conditions for approximately the same time. By means of microscopic examination, physical performance testing and chemical composition analysis, eight ACGT-HP seal failure reasons that act synergistically are summarized and preventive improvement measures are proposed. It is concluded that the main reason for the failure of the combined seal is that the wear of the seal is exacerbated by various inappropriate coupling factors, resulting in the rapid reduction of contact stress and seal failure. By putting forward the key points of seal design and assembly, a universal failure analysis method from macro to micro is found, which can be used for various types of combined seals to guide the design and application of the ACGT-HP seals. [ABSTRACT FROM AUTHOR]

In this paper, the tribological performances of polychlorotrifluoroethylene and polyimide compared with those of 0Cr18Ni9 steel at temperatures as low as − 120 °C were investigated, including varying speed and load. The results demonstrated that the coefficient of friction and wear rate of polychlorotrifluoroethylene were lower and more stable than those of polyimide. The excellent self-cleaning performance of polychlorotrifluoroethylene kept its interface smooth and reduced the coefficient of friction and wear rate. The study demonstrated the superior integrity and sealing performance of polychlorotrifluoroethylene in cryogenic environments, and proved its suitability as a valve component at low temperatures. • The wear rate of PI is not sensitive to decreasing temperature. • The wear rate of PCTFE decreases with decreasing temperature. • The COF of PI increases with decreasing temperature. • PCTFE self-cleaning property ensures a smooth interface and a low COF and wear rate. • PCTFE has better wear and tribological properties than PI at low temperatures. [ABSTRACT FROM AUTHOR]

Establishing a correlation between the friction coefficient and friction noise of metal-friction polymer interfaces is a challenging task in various environments. To address this issue, our study utilizes machine learning algorithms to construct a friction data-based model, elucidating the relationship between noise and friction coefficient. We propose the variational mode decomposition (VMD) along with five machine learning algorithms, each capturing unique data characteristics. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods. By comparing the regression results, we develop a tribological model that offers a novel approach for constructing a nonlinear model, enabling precise monitoring of the friction coefficient by leveraging noise signals. [ABSTRACT FROM AUTHOR]