Experimental Evaluation and Neural Network Prediction of Fatigue Life of E-Glass Fiber Reinforced Hybrid Composite Material
参考中译:玻璃纤维增强混杂复合材料疲劳寿命的试验评估与神经网络预测


          

刊名：International review of mechanical engineering
作者：Alsammarraie A.（Mechanical Engineering Department Faculty of Engineering University of Tikrit）
Ahmed S.R.（Mechanical Engineering Department Faculty of Engineering University of Tikrit）
Obaid A.S.Y.（Engineering Affairs Department University of Fallujah）
刊号：780MC007
ISSN：1970-8734
出版年：2022
年卷期：2022, vol.16, no.8
页码：407-419
总页数：13
分类号：TH
关键词：ANFIS；Epoxy；Fatigue Life；Glass Fiber；Novolac；Stress
参考中译：ANFIS；环氧树脂；疲劳寿命；玻璃纤维；酚醛；应力
语种：eng
文摘：The present study focuses on investigating the impact of hybrid composite content and loading parameters on the fatigue life of E-glass fiber-reinforced composite. A mathematical model of the fatigue life was developed based on experimentation and expressed as a function of the composite content (Epoxy content (80%, 60%) wt., Novolac content (20%, 40%) wt., E-Fiber Glass content (10%, 20%, and 30%) wt.), and different loads using Adaptive Neural Fuzzy Inference Systems (ANFIS). In order to validate the ANFIS model, 30 fatigue cycle experiments were conducted. Using the experimental data sets, the ANFIS was trained with a momentum algorithm and an average absolute percentage error of 6.045% was obtained and compared to the output of the conventional Artificial Neural Network (ANN) Model. The testing accuracy was then verified with 6 extra experimental data sets and the average predicting error was 5.18%. the predicted ANFIS output data were then used to discuss the effect of the composite content and loading parameters on the fatigue life. The number and shapes of cracks observed on the failed specimens were analyzed under an optical microscope. The analysis revealed that the loading impact showed the highest influence on fatigue life for all types of enforcement and the sequence of strength was E-glass fiber, epoxy, and Novolac contents, respectively.
参考中译：本文主要研究混杂复合材料含量和加载参数对玻璃纤维增强复合材料疲劳寿命的影响。在试验的基础上建立了疲劳寿命的数学模型，并利用自适应神经模糊推理系统(ANFIS)将其表示为复合材料含量(环氧树脂含量(80%，60%)，酚醛树脂含量(20%，40%)，E-玻璃纤维含量(10%，20%，30%)重量)和不同载荷的函数。为了验证ANFIS模型的有效性，进行了30次疲劳循环试验。利用实验数据集，用动量算法对神经网络进行训练，得到了6.045%的平均绝对误差，并与传统的人工神经网络模型的输出进行了比较。用6组额外的实验数据验证了模型的测试精度，平均预测误差为5.18%。然后利用预测的ANFIS输出数据讨论了复合材料含量和加载参数对疲劳寿命的影响。在光学显微镜下对失效试件上观察到的裂纹数量和形状进行了分析。分析表明，加载冲击对疲劳寿命的影响最大，强度大小依次为E玻璃纤维、环氧树脂和酚醛树脂含量。



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