Research of the accelerated fatigue experiment method based on the particle filtering algorithm method and the theory of crack growth. (April 2023)

• Record Type: Journal Article
• Authors: SongSong, Sun; Yu, Hou; Xiaolin, Gong
• Is Part Of: Theoretical and applied fracture mechanics. Volume 124(2023)
• Publication Date: 2023-04
• Subjects: Crankshaft -- Accelerated fatigue experiment -- Particle filtering algorithm -- Fatigue limit load; Fracture mechanics -- Periodicals; 620.1126
• DOI: 10.1016/j.tafmec.2022.103746
• Languages: English
• ISSNs: 0167-8442

Abstract

Highlights: The remaining life prediction research in the fatigue research field. A new modification of the sampling range based on the theory of fracture mechanics. Both predictions and experimental verifications. Abstract: For the critical engine parts such as the crankshafts, the fatigue property is necessary for the guidance in actual engineering applications. At present, this property is usually evaluated by the standard fatigue experiment, which is time-consuming and expensive. In this paper, an accelerated fatigue experiment method was developed to shorten the period of the crankshaft fatigue experiment. First the residual fatigue life of the crankshaft was predicted based on the particle filtering algorithm method. Then the system state-space equations was modified based on the theory of fracture mechanics to improve the accuracy of the predictions. Finally the statistical analysis based on the predicted data was adopted to determine the fatigue limit load of the crankshaft. The result showed that this method could provide nearly the same statistical analysis result with that obtained from the standard experiment process (the relative difference is less than 2%), and the experiment period was cut down by 20% or more, which makes it feasible for actual engineering applications.