A Bayesian Approach to Lamb-Wave Dispersion Curve Material Identification in Composite Plates

Guided waves are gaining increased interest in SHM, thanks to some distinct advantages. For guided-wave-based localisation strategies, information on the group velocity is required; therefore, determination of accurate dispersion curves is invaluable. However, for complex materials, the wave speed is dependent on the propagation angle. From experimental observations of dispersion curves, measured using a two-dimensional Fourier transform, a system identification procedure can be used to determine the estimated value and distribution for the governing material properties. Markov-chain Monte Carlo (MCMC) sampling can provide a way of simulating samples from these distributions, which would require solving dispersion curves many times. By using a novel Legendre polynomial expansion approach, the computational cost of dispersion curve solutions is greatly reduced, making the MCMC procedure a more practical approach In this work, a scanning laser Doppler vibrometer is used to record the propagation of Lamb waves in a carbon-fibrecomposite plates, and points on the dispersion curve are extracted. These observations are then fed into the MCMC material identification procedure to provide a Bayesian approach to determining properties governing Lamb wave propagation at various angles in the plate. The distribution of parameters at each angle is discussed, including the inferred confidence in the predicted parameters.