Damage identification in fiber metal laminates using Bayesian analysis with model order reduction

Fiber metal laminates (FML) are composite structures consisting of metals and fiber reinforced plastics (FRP) which have experienced an increasing interest as the choice materials in aerospace automobile industries. Due to a sophisticated built up material, not only design production such is challenging but also its damage detection. This research work focuses on identification FML with guided ultrasonic waves (GUW) through inverse approach based Bayesian paradigm. As inference involves multiple queries underlying system, parameterized reduced-order model (ROM) used closely approximate solution considerably less computational cost. The signals measured by embedded sensors ROM forecasts employed for localization characterization FML. In this paper, Markov Chain Monte-Carlo (MCMC) Metropolis–Hastings (MH) algorithm Ensemble Kalman filtering (EnKF) technique deployed identify damage. Numerical tests illustrate approaches results compared regard accuracy efficiency. It found that both methods successful multivariate high were able quantify their associated uncertainties. EnKF distinguishes itself MCMC-MH matter application identifying damage, approximately thrice faster than MCMC-MH.