Predicting delamination in composite laminates through semi-analytical dynamic analysis and vibration-based quantitative assessment

Abstract

Delamination is a frequent failure mode of laminated fiber-reinforced polymer (FRP) composites structure in aeronautical and other industries, leading to changes in vibration characteristics. Vibration-based techniques evaluate and compare the dynamic response between damaged and undamaged structures, and guarantee the non-destructive measurement with reliability and repeatability. Previous studies typically concentrate on using finite element method to obtain vibration characteristics and enhance the database for intelligent algorithms. This paper presents a semi-analytical result using the Chebyshev−Ritz method to expand delamination prediction. Vibration frequency serves as a global damage indicator, and multi-order frequency characteristics are utilized to identify the delamination length and location of FRP composite plates. A database of natural frequencies corresponding to damage parameters for FRP laminated plates is generated based on the established model using the region approach. An intelligent approach, known as a genetic algorithm optimization-based back-propagation (GA-BP) artificial neural network, is utilized for system identification. The network model is subjected to a sensitivity analysis, where artificial noise is added to vibration frequency to distinguish between the actual structure and the numerical model. The results indicate that the GA-BP algorithm shows good accuracy and stable performance against the standard neural networks for delamination analysis.