Fiber-reinforced friction materials: Experimental, statistical and computational universal analysis

Abstract

In this paper, the experimental, statistical and computational universal analysis methods applicable to most fiber-reinforced friction materials (FMs) are presented for four natural wollastonite fibers (WFs) with different length-to-diameter (L/D) ratios. Machine learning (ML) methods for friction coefficient prediction of friction materials are compared and screened. Numerical statistics and calculations are used in conjunction with the use of finite element (FE) models to determine the performance metrics and friction wear mechanisms of FMs. The experimental and computational results show that the artificial neural network (ANN) model performs the best in COF prediction with an accuracy of 0.961. The friction fluctuation, friction stability and integrated friction performance of the WF-containing samples are better than those of the blank samples. For the first time, the FE model was used to explain the swirling phenomenon on the wear surface of FMs. This study provides new ways and ideas for the design, experimentation, data processing and application of friction composites.