journal article·2022-02

Braking behaviours of C/C–SiC mated with iron/copper-based PM in dry, wet and salt fog conditions

Huixian Tang, Pengtao Li, Zhuan Li, Jinwei Li, Jiong Zhao, Yang Xu, Yang Li, Peng Xiao

Ceramics International· Volume 48· Issue 3
Publisher page

Abstract

Abstract C/C–SiC composites have enormous potential as a new generation of brake materials. It is worth studying the friction and wear behaviours of these materials in special environments to ensure the safe and effective braking of trains in practical applications. In this study, the braking behaviours and wear mechanisms of C/C–SiC mating with iron/copper-based PM in dry, wet and salt fog conditions are compared in detail. The results show that the coefficient of friction (COF) in the wet condition is reduced by 14.13% compared with that under the dry condition. The COF value of the first braking under salt fog condition is increased by 12.27% and 30.75% compared to the dry and wet conditions, respectively. Additionally, the tail warping phenomenon of the braking curve disappears in wet condition, which is attributed to the weak adhesion of friction interfaces and the lubrication of the water film. The main wear mechanisms of C/C–SiC mating with iron/copper-based PM under dry condition are adhesive, fatigue and oxidation wear. However, the dominant wear in wet condition is abrasive wear. The cooling and lubrication of water reduce the tendency of thermal stress, and weaken adhesive and fatigue wear. Furthermore, salt fog can accelerate the corrosion of alloy friction film, leading to the damage of friction film. Meanwhile, the third body particles formed in salt fog condition participate in the braking process. The wear mechanisms in salt fog condition are dominated by abrasive and delamination wear.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Brake Systems and Friction Analysis · Tribology and Wear Analysis · Mechanical stress and fatigue analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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