Identification of material properties - efficient modelling approach based on guided wave propagation and spatial multiple signal classification

• Published in: Structural control and health monitoring Vol. 22; no. 7; pp. 969 - 983
• Main Authors: Ambrozinski, L., Packo, P., Pieczonka, L., Stepinski, T., Uhl, T., Staszewski, W. J.
• Format: Journal Article
• Language: English
• Published: Pavia Blackwell Publishing Ltd 01.07.2015; John Wiley & Sons, Inc
• Subjects: Aluminum; Dispersions; Experimental methods; Finite difference method; graphical cards; guided waves; Image processing; inverse problem; material constants identification; Mathematical analysis; Mathematical models; Modelling; numerical modelling; Physical properties; Signal classification; spatial multiple signal classification; Wave propagation
• ISSN: 1545-2255; 1545-2263
• DOI: 10.1002/stc.1728

Summary Modern structures are often designed using new types of lightweight materials of interesting properties. Accurate information on physical properties of these materials is a key element of every stage of lifecycle from design through maintenance to retirement. Although there are numerous experimental methods that can be used for material testing, only a small handful of these methods provide required information on material parameters in a nondestructive and in‐operational manner, assuring high level of accuracy. The paper demonstrates application of a method that can be used to estimate material properties of engineering structures. The method is based on guided wave propagation and dispersion characteristics. The proposed approach combines three recently developed elements, that is, efficient numerical, experimental and image processing analyses: (i) wave propagation modelling is based on two finite difference approaches, that is, the semianalytical finite difference method and the 3‐D local interaction simulation approach implemented with a multigeneral‐purpose computing on graphics processing units platform to avoid numerical discrepancies and to reduce the computational effort; (ii) experimental testing utilises noncontact, scanning laser vibrometry; and (iii) image processing involves spatial multiple signal classification to improve dispersion curve estimation. This unique combination offers a reliable approach for material parameter estimation. The proposed method is fully nondestructive and can be performed online under varying operational conditions. The method is demonstrated using Young's modulus estimation of an aluminium plate. The results are compared using the traditional destructive approach based on a three‐point bending test. Copyright © 2015 John Wiley & Sons, Ltd.