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兰州理工大学 机电工程学院,兰州 730050
张鹏,男,1999年生,甘肃庆阳人,硕士研究生;主要研究方向为复合材料力学性能;E-mail:2595577424@qq.com。
刘俭辉,男,1985年生,河南商丘人,博士,教授,硕士研究生导师;主要研究方向为机械强度理论;E-mail:liujh@lut.edu.cn。
收稿日期:2023-11-23,
修回日期:2024-01-15,
纸质出版日期:2025-07-15
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张鹏, 刘俭辉, 韦尧兵.边缘冲击下T300/69层合板压-压疲劳寿命预测模型[J].机械强度,2025,47(7):86-92.
ZHANG Peng,LIU Jianhui,WEI Yaobing.Compression-compression fatigue life prediction model of T300/69 laminates under edge impact[J].Journal of Mechanical Strength,2025,47(7):86-92.
张鹏, 刘俭辉, 韦尧兵.边缘冲击下T300/69层合板压-压疲劳寿命预测模型[J].机械强度,2025,47(7):86-92. DOI: 10.16579/j.issn.1001.9669.2025.07.011.
ZHANG Peng,LIU Jianhui,WEI Yaobing.Compression-compression fatigue life prediction model of T300/69 laminates under edge impact[J].Journal of Mechanical Strength,2025,47(7):86-92. DOI: 10.16579/j.issn.1001.9669.2025.07.011.
边缘低速冲击后复合材料层合板内部产生分层、基体挤压等损伤,对于复合材料层合板的安全使用和寿命会产生严重影响。因此,建立一种针对边缘低速冲击后的疲劳寿命预测模型具有实际工程意义。通过低速冲击试验、压缩试验和压-压疲劳试验获得疲劳寿命预测模型的凹陷损伤尺寸、压缩剩余强度和疲劳寿命等数据。基于平均应力破坏准则,结合开口等效法,将层合板冲击损伤区域等效为相应孔径,提出不同冲击能量的等效损伤系数,建立了考虑冲击损伤层合板压缩剩余强度的疲劳寿命预测模型,并将预测结果与试验结果进行对比。结果表明,该模型的疲劳寿命预测精度较高,误差控制在10%以内,具有较好的预测能力。
After low-velocity impact at the edge
delamination and matrix extrusion occur inside the composite laminates
which will have a serious impact on the safe use and life of the composite laminates. Therefore
it is of practical engineering significance to establish a fatigue life prediction model for low-velocity impact at the edge. The dent damage size
compressive residual strength and fatigue life of the fatigue life prediction model were obtained by low-speed impact test
compression test and compression-compression fatigue test. Based on the average stress failure criterion
the impact damage area of the laminated plate was equivalent to the corresponding aperture by combining the opening equivalent method
and the equivalent damage coefficient of different impact energy was proposed. A fatigue life prediction model considering the compressive residual strength of impact damaged laminates was established
and the prediction results were compared with the test results. The results show that the fatigue life prediction accuracy of the model is high
the error is controlled within 10%
and the model has good prediction ability.
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