Моделирование характеристик износа при скольжении композиционного материала на основе политетрафторэтилена (ПТФЭ), армированного углеродным волокном, в паре трения с SS304 (12Х18Н10Т)

Abstract

Introduction. Over the last decade, composite materials based on polytetrafluoroethylene (PTFE) have been increasingly used as alternative materials for automotive applications. PTFE is characterized by a low coefficient of friction, hardness and corrosion resistance. However, this material has a high wear rate. A group of researchers attempted to improve the wear resistance of PTFE material by reinforcing it with different fillers. The purpose of the work: This study experimentally investigates the dry sliding wear characteristics of a PTFE composite reinforced with carbon fiber (35 wt.%) compared to SS304 stainless steel. In addition, experimental mathematical and ANN models are developed to predict the specific wear rate, taking into account the influence of pressure, sliding speed, and interface temperature. The methods of investigation. Dry sliding experiments were performed on a pin-on-disk wear testing machine with varying the normal load on the pin, disk rotation, and interface temperature. Experiments were planned systematically to investigate the effect of input parameters on specific wear rates with a wide range of design space. In total, fifteen experiments were carried out at a 5-kilometer distance without repeating the central run experiment. Sliding velocities were obtained by selecting the track diameter on the disk and corresponding rotation of the disk. A feedforward back-propagation machine learning algorithm was used to the ANN model. Results and Discussion. This study finds better prediction accuracy with the ANN architecture having two hidden layers with 150 neurons on each layer. This study finds an increase in specific wear rates with normal load, sliding velocity, and interface temperature. However, the increase is more prominent at higher process parameters. The normal load followed by sliding velocity most significantly affects the specific wear rate. The results predicted by the developed models for specific wear rates are in good agreement with the experimental values with an average error close to 10%. This shows that the model could be reliably used to obtain the wear rate of PTFE composite reinforced with carbon fiber (35 wt.%) compared to SS304 stainless steel. This study finds scope for further studies considering the effect of varying ANN architectures, different amount of neurons, and hidden layers on the prediction accuracy of the wear rate.