Ultrasonic Guided Waves Based Identification of Elastic Properties Using 1D-Convolutional Neural Networks

Abstract

Identification of elastic properties is crucial for nondestructive material characterization as well as for in-situ condition monitoring. In this paper, we have used ultrasonic guided waves for the identification of elastic properties of a unidirectional laminate with stacked transversely isotropic lamina. The forward problem is formulated and solved using the Spectral Finite Element Method. The data collected from the forward model is utilized to solve the inverse problem of property identification. A supervised regression-based 1D-Convolutional Neural Network is trained with ultrasonic guided wave modes as inputs and elastic properties as targets. The performance of the network is evaluated based on mean squared loss, mean absolute error, and coefficient of determination. It is seen that such deep networks can learn the unknown mappings and generalize well on unseen examples.