Vibration-based inverse algorithms for detection of delamination in composites

Abstract

Delamination is a frequent and potentially serious damage that can occur in laminated polymer composites due to the poor inter-laminar fracture toughness of the matrix. Vibration based detection methods employ changes caused by loss of stiffness in dynamic parameters such as frequencies to detect and assess damage. One of the challenges of using frequency shift for damage detection is that while the presence of damage is easily identified through a shift in measured frequency, the determination of the location and the severity of the damage are not easy to accomplish. To determine the location and severity of damage from measured changes in frequency, it is necessary to solve the inverse problem, which requires the solution of a set of non-linear simultaneous equations. In this paper, we examine three different inverse algorithms for solving the non-linear equations to predict the interface, lengthwise location and size of delamination: direct of solution using a graphical method, artificial neural network (ANN) and surrogate-based optimization. The three inverse algorithms have been validated using numerical data generated from the finite element model (FEM) of delaminated beams and measured frequencies from modal testing conducted on simply supported and cantilever carbon fiber reinforced beam specimens. Results show that all three algorithms can predict the delamination parameters accurately using the validation data directly generated from FE model. However, if using experimental data from real beams, ANN does not fare as well as the other two methods as it is more sensitive to the measurement errors. Finally, the advantages and limitations of each method have been summarized to provide a useful guide for selecting inverse algorithms for vibration-based delamination detection.