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首页> 外文期刊>International Journal of Sustainable Materials and Structural Systems >Fatigue life prediction for carbon fibre/epoxy laminate composites under spectrum loading using two different neural network architectures
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Fatigue life prediction for carbon fibre/epoxy laminate composites under spectrum loading using two different neural network architectures

机译：使用两种不同的神经网络架构施加纤维/环氧树脂层压复合材料的疲劳寿命预测

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摘要

The objective of this study is to predict the fatigue life of carbon fibre/epoxy composite laminate sheets involving 12 balanced woven bidirectional layers with the same orientation angle [0/90°]. The composite sheets considered are subjected to variable amplitude block loadings with different negative and positive stress ratios. This objective is accomplished by designing an efficient artificial neural network (ANN) architecture, with taking into account effect of the residual strength from spectrum loading. The number of cycles to failure (N) is related to the residual strength of the structure for constant amplitude loading. A simple first order model is postulated that determines the residual strength at any point during the fatigue life as a function of the static strength and stress ratio by applying the two-parameter Weibull probability density distribution. Two neural network structures, a feed-forward neural network (FFNN) and a radial basis neural network (RBNN), are applied, trained and tested to predict the fatigue life based on four groups of data considered. These data include the maximum stress (σ_(max)) and the stress ratio (R), the Smith-Watson-Topper (SWT) parameter, fatigue strength ratio (Ψ), or failure criterion with the fibre orientation. On the other hand, the validity of the SWT, Ψ and selected suitable failure criterion for present study including material, loading and orientation were checked and modified before they were used for training the designed ANNs. The results show improvement when using one input data (SWT, or Ψ, or failure criterion) instead of two input data (σ_(max) and R) and for the case of one input data the best prediction is observed for failure criterion condition, followed by Ψ and SWT respectively. Moreover, the RBNN demonstrates better results as compared with those obtained by the FFNN.
机译：本研究的目的是预测涉及具有相同取向角度的12平衡编织双向层的碳纤维/环氧复合层压板的疲劳寿命[0/90°]。所考虑的复合片材经受具有不同负极和正应力比的可变幅度块负载。该目的是通过设计高效的人工神经网络（ANN）架构来实现的，考虑到频谱载荷的残余强度。失败的循环次数（n）与恒定幅度负载的结构的残余强度有关。假设一个简单的第一阶模型，其通过应用双参数Weibull概率密度分布来确定疲劳寿命期间的任何点的残留强度。施加，训练和测试两个神经网络结构，前馈神经网络（FFNN）和径向基神经网络（RBNN），以基于考虑的四组数据来预测疲劳寿命。这些数据包括最大应力（σ_（max））和应力比（R），史密斯 - 沃特森 - 汤底（SWT）参数，疲劳强度比（ψ）或具有纤维取向的故障标准。另一方面，在将包括材料，装载和方向的目前研究包括材料，装载和方向的目前研究的适当失效标准的有效性进行了检查和修改。结果显示使用一个输入数据（SWT或ψ或故障标准）而不是两个输入数据（Σ_（max）和r），并且对于一个输入数据的情况，对于故障标准条件，观察到最佳预测，然后分别为ψ和swt。此外，与由FFNN获得的那些相比，RBNN展示了更好的结果。

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