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超高周疲劳寿命预测方法探讨
引用本文:宋亚南,徐滨士,王海斗,张玉波,邢志国. 超高周疲劳寿命预测方法探讨[J]. 稀有金属材料与工程, 2016, 45(5): 1203-1208
作者姓名:宋亚南  徐滨士  王海斗  张玉波  邢志国
作者单位:装甲兵工程学院,装甲兵工程学院,装甲兵工程学院,装甲兵工程学院,装甲兵工程学院
基金项目:国家重点基础研究发展计划(2011CB013405);国家杰出青年科学基金(51125023)
摘    要:分析了金属材料超高周疲劳断口形貌特征,介绍了基于Paris公式的裂纹扩展寿命预测模型和基于位错理论的疲劳裂纹萌生寿命预测模型,并结合前期有关金属材料超高周疲劳行为的试验数据,对2种预测模型的误差进行分析。结果表明,基于位错理论的寿命预测模型较为准确;而基于Paris公式的裂纹扩展寿命预测模型,其预测精度随着疲劳寿命的增加而降低,即材料组织缺陷萌生成为疲劳裂纹阶段占据疲劳寿命的绝大部分。在此基础上,提出了超高周疲劳寿命预测的研究方向:疲劳裂纹的萌生机制,特别是裂纹源表面萌生和内部萌生的竞争性机制;建立大样本数据,结合统计学方法,以工程构件的服役安全性和可靠性为基础,精确评价超高周疲劳寿命。

关 键 词:超高周疲劳  寿命预测  断口形貌  预测误差
收稿时间:2015-04-01
修稿时间:2015-06-07

Discuss on the Life Prediction in Very High Cycle Fatigue Regime
Song Yanan,Xu Binshi,Wang Haidou,Zhang Yubo and Xing Zhiguo. Discuss on the Life Prediction in Very High Cycle Fatigue Regime[J]. Rare Metal Materials and Engineering, 2016, 45(5): 1203-1208
Authors:Song Yanan  Xu Binshi  Wang Haidou  Zhang Yubo  Xing Zhiguo
Affiliation:Academy of Armored Forces Engingeering,Academy of Armored Forces Engingeering,Academy of Armored Forces Engingeering,Academy of Armored Forces Engingeering,Academy of Armored Forces Engingeering
Abstract:The typical characteristics of S-N curve and the fracture surface in very high cycle fatigue regime were analyzed in this paper. The fatigue lifetime prediction models based on crack growth and dislocation theory were introduced. On the basis of the numerical data of previous researches on very high cycle fatigue, the prediction errors of the two modes were analyzed. The results indicate that the model based on dislocation theory has better prediction ability. The prediction accuracy decreases with the increment of fatigue lifetime. That is to say, the fatigue crack initiation consumes the main fraction of total life. Finally, some possible and prospective topics of future researches on fatigue lifetime prediction in very high cycle fatigue regime are proposed: the competing failure mechanism of surface/internal fatigue fracture modes, the lifetime prediction in very high cycle fatigue regime based on statistical methods by establishing large samples data.
Keywords:very high cycle fatigue   lifetime prediction   fracture surface   prediction error
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