Locating Defects in Anisotropic CFRP Plates Using ToF-Based Probability Matrix and Neural Networks
Locating Defects in Anisotropic CFRP Plates Using ToF-Based Probability Matrix and Neural Networks
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DOI：
10.1109/tim.2019.2893701
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发表时间：
2019-05-01
影响因子：
5.6
通讯作者：
Ramos, Helena Geirinhas
中科院分区：
文献类型：
--
作者：
Feng, Bo;Pasadas, Dario Jeronimo;Ramos, Helena Geirinhas
提出了两种基于散射波飞行时间（ToF）的各向异性编织物碳纤维增强聚合物（CFRP）板缺陷定位算法。第一种算法通过构造概率矩阵来使用概率方法。该矩阵的每个元素都与一个位置相关联，并表示在板的相应空间坐标上存在缺陷的概率。对于概率矩阵法，局部化结果受人工选择的参数和CFRP板的各向异性的影响。第二种算法，基于人工神经网络（ann），使我们能够提高缺陷定位的准确性。利用这种方法，神经网络可以从训练数据中“学习”各向异性特征。将三对传感器的散射波ToF直接作为神经网络的输入。缺陷的空间坐标是人工神经网络的输出。通过在待测板表面添加块来模拟缺陷，克服了获取足够实验数据进行人工神经网络训练的困难。通过对分层和添加块体的散射波进行比较，验证了该方案的有效性。通过对两种方法的比较，得出了结论。结果表明，人工神经网络算法的定位效果较好。
This paper presents two algorithms, both based on the time of flight (ToF) of scattered waves, to locate defect in an anisotropic woven-fabric carbon fiber reinforced polymer (CFRP) plate. The first algorithm uses a probabilistic approach by constructing a probability matrix. Each element of this matrix is associated with a location and represents the probability of existing a defect at the corresponding spatial coordinates of the plate. For the probability matrix method, localization results are influenced by manually chosen parameters and by the anisotropy of the CFRP plate. The second algorithm, based on artificial neural networks (ANNs), enables us to improve the accuracy of locating defects. With this ANN method, the anisotropic feature can be "learned" by the neural network from training data. The ToF of scattered waves obtained from three sensor pairs were used directly as inputs of the neural network. The spatial coordinates of the defect are the ANN outputs. The difficulty of obtaining sufficient experimental data for ANN training was surpassed by using added blocks on the surface of the plate under test to simulate defects. This scheme was validated and proved to be effective by comparing the scattered waves from a delamination and from an added block. Conclusions have been drawn by comparing the two methods. The localization results obtained by the ANN algorithm are proved to be better.