Barely visible impact damage detection in composite structures using deep learning networks with varying complexities
Barely visible impact damage detection in composite structures using deep learning networks with varying complexities
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DOI：
10.1016/j.compositesb.2023.110907
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发表时间：
2023-07
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通讯作者：
A. Tabatabaeian;Bruno Jerkovic;P.V. Harrison;E. Marchiori;M. Fotouhi
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作者：
A. Tabatabaeian;Bruno Jerkovic;P.V. Harrison;E. Marchiori;M. Fotouhi
目视检查是最常见的无损检测（NDT）方法之一，可快速评估航空航天复合材料结构的表面损伤。然而，它高度依赖于人为因素，可能无法检测到几乎不可见的撞击损伤（BVID）。本研究针对两组复合材料面板，即“参考”与“传感器整合”样本，进行不同能量水平的低速撞击实验。然后，分析冲击测试的结果以及C扫描和视觉检查图像，以定义BVID范围并创建原始图像数据集。接下来，对四种不同的深度学习模型进行训练、验证和测试，以仅从受影响和未受影响表面的图像中捕获BVID。结果表明，所有四个网络都可以很好地学习和检测BVID，传感器集成样本减少了训练时间，提高了深度学习模型的准确性。ResNet优于其他网络，在参考和传感器集成样本的背面上分别具有96.2%和98.36%的最高准确度。所提出的损伤识别方法可以作为一种快速，廉价和准确的结构健康监测工具，在实际应用中的复合材料结构。
Visual inspection is one of the most common non-destructive testing (NDT) methods that offers a fast evaluation of surface damage in aerospace composite structures. However, it is highly dependent on human-related factors and may not detect barely visible impact damage (BVID). In this research, low velocity impact tests with different energy levels are conducted on two groups of composite panels, namely ‘reference’ and ‘sensor-integrated’ samples. Then, the results of impact tests, together with C-scan and visual inspection images, are analysed to define the BVID range and create an original image dataset. Next, four different deep learning models are trained, validated and tested to capture the BVID only from the images of the impacted and non-impacted surfaces. The results show that all four networks can learn and detect BVID quite well, and the sensor-integrated samples reduce the training time and improve the accuracy of deep learning models. ResNet outperforms other networks with the highest accuracy of 96.2% and 98.36% on the back-face of reference and sensor-integrated samples, respectively. The proposed damage recognition method can act as a fast, inexpensive and accurate structural health monitoring tool for composite structures in real-life applications.