Lamb Wave Propagation-based Damage Identification for Quasi-isotropic CF/EP Composite Laminates Using Artificial Neural Algorithm: Part I - Methodology and Database Development

Abstract

A guided Lamb wave-based damage identification scheme and an online structural health monitoring (online-SHM) system with an integrated piezoelectric actuator-sensor network are developed. The proposed methodology is applied to the quantitative diagnosis of through-hole-type defect in the CF-EP quasi-isotropic laminate with the aid of an artificial neural network algorithm. For this purpose, a variety of composite laminates with stochastic damages are considered, and the corresponding three-dimensional dynamic FEM simulations are conducted. To describe a Lamb wave excited by the PZT actuator, models for both the piezoelectric actuator and sensor coupled with the composite laminates are established. A wavelet transform-based signal processing package (SPP) is devised to purify the acquired wave signals, and further extract characteristics from the energy spectra of Lamb waves over the time-scale domain. A concept of ‘digital damage fingerprints’ is introduced, with which a damage parameters database (DPD) is constructed and used to offline train a multilayer feedforward neural network, supervised by an error-back propagation (BP) neural algorithm. Such an identification technique is then validated, to be described in the second part of this study.