Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites
Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites
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DOI：
10.1088/1361-665x/ac093d
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发表时间：
2021-06
影响因子：
4.1
通讯作者：
J. Nasser;L. Groo;H. Sodano
中科院分区：
文献类型：
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作者：
J. Nasser;L. Groo;H. Sodano
纤维增强聚合物基复合材料的机械性能是已知的逐渐恶化的疲劳损伤累积下循环加载条件。虽然在整个疲劳寿命中弹性刚度的稳定退化是一个公认的和研究的概念，但在许多动态工程系统的使用寿命期间，仍然难以连续监测这种结构变化，其中复合材料受到随机和意外的载荷条件。最近，激光诱导石墨烯（LIG）已被证明是一个可靠的，在原位应变传感和损伤检测组件在玻璃纤维复合材料在准静态和动态加载条件下。这项工作探讨了利用LIG夹层玻璃纤维复合材料的压阻特性，以制定累积损伤参数和预测损伤进展和疲劳寿命，使用人工神经网络（ANN）和传统的唯象模型的潜力。LIG夹层玻璃纤维复合材料进行拉伸-拉伸疲劳载荷，而它们的弹性刚度和电阻的变化通过被动测量监测。根据电阻的变化定义的损伤参数被发现是能够准确地描述在LIG夹层玻璃纤维复合材料在整个疲劳寿命的损伤进展，因为它们显示出类似的趋势的基础上的弹性刚度的变化。这些损伤参数，然后利用预测的疲劳寿命和未来的损伤状态的玻璃纤维复合材料，使用训练的人工神经网络和现象学的退化和积累模型，在两个样本和周期到周期的计划。当用于样本到样本方案时，发现每层40个神经元的双层贝叶斯正则化ANN的预测比现象学退化模型的预测准确至少60%，显示R2值大于0.98，均方根误差（RMSE）值小于10−3。一个两层贝叶斯正则化神经网络，每层25个神经元，也被发现产生准确的预测时，用于一个周期到周期的计划，显示R2值大于0.99和RMSE值小于2 × 10−4一旦超过30%的初始测量值被用作输入。最终结果证实，压阻LIG夹层是一个很有前途的工具，实现准确和连续的疲劳寿命预测的多功能复合材料结构，特别是当再加上机器学习算法，如人工神经网络。
The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R 2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R 2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs.