| 王晓玮,尚德广,熊健.多轴载荷下结构细节疲劳强度额定值确定方法[J].装备环境工程,2018,15(3):92-97. WANG Xiao-wei,SHANG De-guang,XIONG Jian.Determination of Detail Fatigue Rating of structure under Multiaxial Loading[J].Equipment Environmental Engineering,2018,15(3):92-97. |
| 多轴载荷下结构细节疲劳强度额定值确定方法 |
| Determination of Detail Fatigue Rating of structure under Multiaxial Loading |
| 投稿时间：2018-01-15 修订日期：2018-03-15 |
| DOI：10.7643/ issn.1672-9242.2018.03.019 |
| 中文关键词: 细节疲劳强度额定值 双点法 多轴疲劳试验 多轴高周疲劳 |
| 英文关键词:detail fatigue rating two-point method multiaxial fatigue test multiaxial high-cycle fatigue |
| 基金项目:国家自然科学基金（11272019, 51535001, 11572008） |
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| 中文摘要: |
| 目的 提出一种在多轴载荷下确定结构细节疲劳强度额定值的方法。方法 基于单轴双点法细节疲劳强度额定值（DFR）确定方法，在多轴载荷下，首先利用高周多轴疲劳损伤模型求出其等效应力幅（等效拉伸应力幅或等效剪应力幅），结合Goodman方程，把等效应力幅转换为应力比R=0.06时的最大正应力，最终确定多轴条件下的细节疲劳强度额定值。通过7075-T651铝合金薄壁管进行单轴疲劳试验，确定单轴细节疲劳强度额定值，并进行多轴疲劳试验，包括比例加载和非比例加载。结果 采用该方法预测多轴载荷下的DFR值，对比单轴试验的DFR值，相对误差的绝对值在10%左右。结论 该方法确定多轴条件下的结构细节疲劳强度额定值具有较好的效果。 |
| 英文摘要: |
| Objective To propose a method for determining Detail Fatigue Rating (DFR) of structure under multiaxial loading. Methods Based on the two-point method for uniaxial loading (DFR), the high-cycle multiaxial fatigue test model was adopted to obtainthe equivalent stress (equivalent tensile stress or equivalent shearing strength). The equivalent stress was transferred to the equivalent tension stress under R=0.06 through the Goodman equation, to determine the detail fatigue rating under multiaxial loading. The equivalent stress was determined by employing a multiaxial high-cycle fatigue model. According to the uniaxial fatigue tests for 7075-T651 aluminum alloy under R=0.06, the DFR for uniaxial loading was determined. Then, the multiaxial fatigue tests were conducted, including the proportional and non-proportional loadings. Results From the comparisons between the predicted DFR for multiaxial loading and the experimental DFR for uniaixal loading, the absolute value of the relative errors was about 10%. Conclusion The proposed method, for the determination of the DFR under multiaxial, has a good predictive capability. |
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