Prediction of the tribological properties of a polymer surface in a wide temperature range using machine learning algorithm based on friction noise

Cheng, Ganlin; Xiang, Chong; Guo, Fei; Wen, Xiaohao; Jia, Xin

doi:10.1016/j.triboint.2022.108213

Cited by 14 publications

(8 citation statements)

References 40 publications

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“…Besides, prediction could be established based on the identification of the link between the contact roughness and the frictioninduced vibration. Some authors proposed the variation mode decomposition along with ML algorithms to predict the COF [31,32]. Algorithm optimization is achieved through the implementation of L1 and L2 regularization methods.…”

Section: Identification and Classification Of Wear Modes And Wear Debrismentioning

confidence: 99%

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

Kchaou

2024

Sustainable Engineering and Innovation

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Tribology problems generally, and particularly high-temperature tribology (HTT), is a critical and complex topic based on the interaction between several intrinsic and extrinsic parameters. This involved complex phenomena, resulting in synergistic effects between mechanical, physical, chemical, and thermal solicitations. Introducing artificial intelligence tools, coupled with the design of the experiment, is an original approach to implement a successful transition from traditional "experimental guidance" to "experimental guidance associated with a data-driven" approach. The current study delves into the utilization of machine learning (ML) with simulation to help in the choice of the parameters for experimentation, and the development of predictive models. A detailed framework that takes into account the coupling between such tools is presented. Different scenarios are discussed to data drive the collaborative schema between the design of experiment, numerical development, and ML algorithms. This approach gives several opportunities such as the identification of the well-impacted parameters, optimization of the experimental design, and the proposition of predictive models. With the suitable proposed model, time loss, production costs, precision results, and man-hours could be saved or improved.

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Section: Identification and Classification Of Wear Modes And Wear Debrismentioning

confidence: 99%

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

Kchaou

2024

Sustainable Engineering and Innovation

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“…Guo and his team introduced a signal-processing method based on friction noise to predict the tribological properties of polymers over a wide temperature range. Their results indicated that ML methods could effectively predict the friction coefficients of different polymer-metal pairs within a broad temperature domain [21]. Through the application of ML, a novel research concept can be proposed for predicting the tribological properties of polymer composites.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning

Yan,

Du,

Ren

et al. 2024

Polymers

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Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. 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. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.

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“…Fatigue experiments were performed on samples at a temperature of 120°C, which is the average operating temperature, the promise of this sealing material has been demonstrated. The work [12] describes a processing technique of noise signals arising from friction to predict the tribological properties of polymers over a wide temperature range. It is shown that the method satisfactorily predicts the friction coefficients of various polymer-metal couplings in a wide temperature range according to tribological tests and can be used to monitor tribological properties.…”

Section: Introductionmentioning

confidence: 99%

Friction of fluoroplastic with steel in a hydraulic fluid medium when transient mode

Alisin

2024

E3S Web Conf.

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The article considers the issues of experimental research of the antifriction properties of fluoroplastic seals of piston pump hydraulic systems in a transient friction mode. The statement about the influence of frictional heating on the tribological properties of the seal is substantiated. Model experiments are used to study the friction coefficients at the moment of changing the direction of the contacting surfaces movement and the transient period from the moment of starting from a standstill to the steadystate friction mode. The experiments were carried out under conditions of heating the hydraulic fluid to the maximum temperature. Particular attention is paid to the change in the friction coefficient with increasing load-speed characteristics. The limiting contact pressures that ensure trouble-free operation of a seal made of radiation-modified fluoroplastic have been determined.

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Prediction of the tribological properties of a polymer surface in a wide temperature range using machine learning algorithm based on friction noise

Cited by 14 publications

References 40 publications

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

A data-driven approach for studying tribology based on experimentation and artificial intelligence coupling tools

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning

Friction of fluoroplastic with steel in a hydraulic fluid medium when transient mode

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