论文标题
使用引导波和具有双分支特征融合的卷积神经网络对复合材料的逆表征
Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion
论文作者
论文摘要
在这项工作中，使用超声波引导波和卷积神经网络的双分支版本用于解决两个不同但相关的逆问题，即查找上式序列类型和识别材料属性。在正向问题中，使用刚度矩阵方法获得了两个基本羔羊波模式的极性速度表示。对于逆问题，实现了基于监督的基于分类的网络，以将极性表示形式分类为不同的上型序列类型（反问题-1），并利用基于回归的网络来识别材料属性（反问题-2）
In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem - 1) and a regression-based network is utilized to identify the material properties (inverse problem - 2)
