A Bayesian Method for Material Identification of Composite Plates via Dispersion Curves

TL;DR

This paper introduces a Bayesian approach using Markov-Chain Monte Carlo to identify material properties of composite plates from ultrasonic guided wave dispersion curves, enhancing non-destructive evaluation accuracy.

Contribution

It develops a Bayesian method employing MCMC sampling to estimate and analyze the distribution of material properties from dispersion curve data in composite plates.

Findings

Successfully applied to glass-fibre composite plates

Provides probabilistic estimates of material properties

Enhances accuracy of structural health monitoring

Abstract

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully-defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber) -- which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate.