Corrosion fatigue mechanism and life prediction of railway axle EA4T steel exposed to artificial rainwater. (August 2022)

• Record Type: Journal Article
• Authors: Li, Hang; Zhang, Jiwang; Wu, Shengchuan; Zhang, Haonan; Fu, Yanan
• Is Part Of: Engineering failure analysis. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: EA4T axle steel -- Corrosion fatigue -- Corrosion pits -- Fatigue life prediction -- Artificial rainwater; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106319
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Effects of the corrosive environment on the fatigue strength of EA4T axle steel are explored. The evolution of corrosion process during fatigue loading is fully investigated. Crack aspect ratio in the corrosive environment is obtained by using synchrotron radiation X-ray computed microtomography (SR-μCT). Fatigue life prediction model is developed based on the pit growth, short crack growth, and long crack growth stages. Abstract: High-speed railway axle is the critical safety component of modern ground vehicles, which experiences extremely complex environments. In this paper, the effect of corrosion on the fatigue behavior of EA4T axle steel was investigated in an artificial rainwater environment. Combining scanning electron microscopy (SEM) and synchrotron radiation X-ray micro computed microtomography (SR-μCT) with theoretical analysis, damage measurement enabled a full evolution characterization of the corrosion process for exploring the corrosion mechanism and its influence on the fatigue life. It is found that the corrosive environment significantly reduced fatigue strength and facilitated multiple crack initiation, while showed relatively little effect on the long fatigue crack growth. The fitness of the 3-P Weibull distribution was slightly higher than that of the Gumbel distribution in terms of crack population. The life prediction model incorporating the pit growth, short crack, and long crack stages showed a good agreement within the 95% and 5% probabilistic S-N curve.