Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

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 identifying material properties. the forward problem, polar group velocity representations obtained for fundamental Lamb wave modes using stiffness matrix method. For supervised classification-based network is implemented classify into types (inverse problem - 1) regression-based utilized identify properties 2)