Ultrasonic guided waves based identification of elastic properties using 1D-Convolutional neural networks

Rautela, M and Gopalakrishnan, S and Gopalakrishnan, K and Deng, Y (2020) Ultrasonic guided waves based identification of elastic properties using 1D-Convolutional neural networks. In: Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM, 8-10 June 2020, Detroit; United States.

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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. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
Publisher:	Prognostics and Health Management Society
Additional Information:	cited By 0; Conference of 2020 IEEE International Conference on Prognostics and Health Management, ICPHM 2020 ; Conference Date: 8 June 2020 Through 10 June 2020; Conference Code:162930
Keywords:	Condition monitoring; Convolution; Elasticity; Guided electromagnetic wave propagation; Inverse problems; Ultrasonic testing; Ultrasonic waves, Coefficient of determination; Elastic properties; Material characterizations; Mean absolute error; Property identification; Spectral finite element method; Transversely isotropic; Ultrasonic guided wave, Convolutional neural networks
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	14 Dec 2020 09:46
Last Modified:	14 Dec 2020 09:46
URI:	http://eprints.iisc.ac.in/id/eprint/66840

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