In situ integrity assessment of a smart structure based on the local material damping
In situ integrity assessment of a smart structure based on the local material damping
复制标题
基于局部材料阻尼的智能结构原位完整性评估
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
W. Hufenbach
中科院分区：
文献类型：
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作者：
P. Kostka;K. Holeczek;A. Filippatos;A. Langkamp;W. Hufenbach
将功能元件集成到纤维增强的主体结构中，可以对关键部件的结构完整性进行现场监测。在这项研究中，基于振动的监测功能已经开发，允许关键部件的结构完整性识别。为此，开发了信号分析算法来估计与损伤相关的模态阻尼。所分析的智能结构是碳纤维增强环氧复合材料板，具有集成的驱动/传感系统。材料的局部阻尼是一个对复合材料的不同破坏模式特别敏感的参数。为了表征该参数在冲击事件下的变化，对复合材料的完整和损坏试样进行了动态力学分析。基于动力力学分析结果，建立了该结构的有限元模型。然后，数值确定了不同尺寸和位置损伤区域的模态阻尼比，并确定了模态阻尼与损伤相关局部阻尼之间的关系。确定了描述在线测量模态阻尼与损伤状态之间反向关系的确定性决策树。这是通过将基于信息熵的数据挖掘算法应用于利用所开发的有限元模型获得的数值生成的学习数据集来实现的。
Integration of functional elements into fibre-reinforced host structures provides the possibility for in situ monitoring of the structural integrity of critical components. In this study, a vibration-based monitoring function has been developed that allows the structural integrity identification of critical components. For this purpose, signal analysis algorithms were developed to enable the estimation of damage-dependent modal damping. The analysed smart structure was a carbon fibre–reinforced epoxy composite plate with an integrated actuating/sensing system. The local material damping is a parameter especially sensitive to different failure modes of composites. In order to characterise the changes of this parameter resulting from impact events, dynamical mechanical analysis on intact and damaged specimens made of the composite material was conducted. Based on the dynamical mechanical analysis results, a finite element model of the structure was developed. Then, modal damping ratios for different sizes and locations of damaged regions were numerically determined, and a relation between modal damping and damage-dependent local damping was identified. The deterministic decision trees describing the reverse relationship between online-measured modal damping and damage condition were determined. That was accomplished through the application of information entropy-based data-mining algorithms to the numerically generated learning dataset obtained using the developed finite element model.