基于深度学习和时间反演的复合材料内扩展性损伤重建

Extended damage reconstruction in composites based on deep learning and time reversal algorithm

复合材料可以获得比单一材料更高的强度、刚度和耐用性,在航空航天、汽车、船舶和建筑等领域中得到了广泛应用。然而,复合材料在制备或使用过程中,可能会产生空洞、裂缝等损伤,这些损伤通常具有连续且不规则的形状,因此实时且准确地重建损伤的形状具有重要意义。基于复合材料电磁逆散射成像方法分别研究了时间反演算法和深度学习中的U-Net算法在解决该问题上的效果,并在此基础上提出了TR-Unet算法,该算法利用多个源时间反演的结果作为模型的输入来预测检测区域的实际相对介电常数分布,进而重构出损伤的形状。然后,对该算法的应用效果进行了检验,试验结果表明,相较于另外两种方法,TR-Unet方法在形状相似度和轮廓细节的准确性上均有显著提升。

Composites can achieve higher strength,stiffness and durability than single materials,and their excellent mechanical properties make them widely used in aerospace,automotive,marine and construction.However,during the preparation or use of composite materials,damages such as voids and cracks may occur,which seriously affect their performance.These damages usually have continuous and irregular shapes in real life,so it is important to reconstruct the shapes of these damages in real time and accurately.In this paper,the effectiveness of time reversal algorithms and the U-Net algorithm in deep learning in solving this problem were investigated respectively based on electromagnetic inverse scattering imaging of composite materials,and based on this,the TR-Unet algorithm was proposed,which utilized the results of multiple source time inversions as inputs to the model to predict the actual relative permittivity distribution of the detected region,and then reconstructed the shape of the damage.The experimental results showed that the TR-Unet method showed significant improvement in both shape similarity and accuracy of contour details compared to the other two methods.