A nonlinear fatigue damage accumulation model considering strength degradation and its applications to fatigue reliability analysis

残余强度 非线性系统 降级(电信) 材料科学 可靠性(半导体) 结构工程 残余物 疲劳极限 振动疲劳 古德曼关系 压力(语言学) 残余应力 应力集中 复合材料 计算机科学 疲劳试验 断裂力学 工程类 算法 电信 功率(物理) 语言学 物理 哲学 量子力学
作者
Rong Yuan,Haiqing Li,Hong‐Zhong Huang,Shun‐Peng Zhu,Huiying Gao
出处
期刊:International Journal of Damage Mechanics [SAGE Publishing]
卷期号:24 (5): 646-662 被引量:69
标识
DOI:10.1177/1056789514544228
摘要

Fatigue is a damage accumulation process in which the material property deteriorates and degenerates continuously under cyclic loading. The analysis of damage accumulation plays a key role in preventing the occurrence of fatigue failures, and the damage evolution mechanism is one of the important focuses of fatigue behavior. In this paper, a residual strength degradation model according to the stress–strength interference (SSI) model is introduced firstly; then a modified nonlinear fatigue damage accumulation model based on the Manson–Halford theory is proposed. Combining the proposed nonlinear damage accumulation model with the residual strength degradation model, a new method is developed for fatigue life prediction under constant and variable amplitude loading, which considers not only the effects of load interactions, but also the phenomenon of strength degradation of materials induced by loading history, and it can be used to predict the reliability and fatigue life of mechanical components. Moreover, the material parameter for the residual strength degradation can be obtained directly from S–N curve without running extra experiments. Finally, experimental data are used to compare with the predicted value in order to demonstrate the proposed residual strength degradation model. In addition, two sets of experimental data are also used to verify the proposed nonlinear fatigue damage accumulation model which is applied to predict fatigue life under two-stress level loading and reliability prediction under multi-stress level loading. The results show that the proposed method has a good agreement between the experimental data and predicted values.

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