纤维增强复合材料层压板的疲劳寿命预测方法

doi:10.16183/j.cnki.jsjtu.2022.312

摘要/Abstract

摘要：

针对纤维增强复合材料层压板的疲劳寿命预测问题,提出了一种将理论与仿真、单向板与多向层压板紧密结合预测疲劳寿命的方法. 基于几组碳纤维/树脂基T300/QY8911单向板在循环应力加载下的疲劳试验数据,通过灰色系统GM(1, 1)模型,建立单向板的疲劳寿命模型和损伤函数. 与传统的最小二乘法拟合相比,该方法提高了预测精度. 通过有限元方法对层压板进行应力分析、失效分析和材料性质退化. 将失效分析的结果与建立的单向板疲劳寿命模型和损伤函数结合,从而来计算多向层压板的疲劳寿命和损伤. 通过与实验数据及其他预测方法的结果对比,该预测方法降低了疲劳寿命预测相对误差,且与实验结果吻合较好.

关键词: 碳纤维复合材料, 灰色系统, 有限元分析, 多向层压板, 疲劳寿命

Abstract:

To address the fatigue life prediction problem of fiber-reinforced composite laminates, a method for predicting fatigue life was proposed by closely combining theory and simulation, unidirectional board and multi-directional laminate. Based on the fatigue test data of several sets of carbon fiber/resin-based T300/QY8911 unidirectional plates under cyclic stress, the fatigue life model and damage function of unidirectional plates were established by using the GM(1, 1) model of the gray system. Compared with the traditional least squares method fitting, this method improves the prediction accuracy. Stress analysis, failure analysis and material degradation of laminates were performed by finite element methods. The results of the failure analysis were combined with the established fatigue life model and damage function of the unidirectional laminate to calculate the fatigue life and damage of the multidirectional laminate. A comparison of the experimental data and those of other prediction methods indicates the prediction method established reduces the relative error of fatigue life prediction and the results predicted is in good agreement with the experimental results.

Key words: carbon fiber composites, gray system, finite element analysis, multi-directional laminates, fatigue life

中图分类号:

TB332

徐蓉霞, 高建雄, 朱鹏年, 吴志峯. 纤维增强复合材料层压板的疲劳寿命预测方法[J]. 上海交通大学学报, 2024, 58(3): 400-410.

XU Rongxia, GAO Jianxiong, ZHU Pengnian, WU Zhifeng. A Fatigue Life Prediction Method for Fiber-Reinforced Composite Laminates[J]. Journal of Shanghai Jiao Tong University, 2024, 58(3): 400-410.

图/表 15

表1

表2

拟合[0]16单向板q-N函数不同方法的精度对比

参数类型	拟合方法	函数表达式	相对误差/%
双参数	最小二乘法	q=-0.035ln N+1.189	63.16
	本文方法	q= $- 0.024 6 l n N + 1.094, 0.8 ≤ q < 1 - 0.078 8 l n N + 1.731, q < 0.8$	6.82
三参数	最小二乘法	q=(1.778×10^-5ln N+2.132)^-6.597	97.838
	本文方法	q= $- 0.024 6 l n N + 1.094, 0.8 ≤ q < 1 (- 0.074 9 l n N + 1.701) 1.090, q < 0.8$	6.684

表2

图1

表3

表4

图2

表5

表6

图3

图4

图5

图6

图7

表7

表8

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q	N
0.90	2.50×10³
0.85	2.40×10⁴
0.80	1.43×10⁵
0.77	1.96×10⁵
0.70	4.57×10⁵

q	N
0.90	1.01×10³
0.80	3.48×10³
0.70	1.59×10⁵
0.60	>10⁶

q	N
0.85	229
0.80	398
0.70	3 350
0.60	2.48×10⁴
0.50	1.78×10⁵
0.40	>10⁶

不同预测方法	疲劳寿命	相对误差/%
文献[23]	236 757	1.612 4
文献[24]	236 768	1.617 2
文献[25]	2.32×10⁵	0.429 2
文献[26]	2.684×10⁵	15.193 1
本文方法	2.331 06×10⁵	0.004 5

不同预测方法	疲劳寿命	相对误差/%
文献[23]	130 800	81.233 9
文献[24]	130 950	81.212 3
文献[25]	2.03×10⁵	70.875 2
文献[26]	9.736×10⁴	86.031 6
本文方法	3.666 1×10⁵	47.401 7