Comparison of Inverse Algorithms for Delamination Detection in Composite Laminates

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 and mode shapes to detect and assess damage. Because it is a whole field method, and can be applied instantaneously and remotely, vibration monitoring using frequency measurements offers great potential for implementation in online structural health monitoring systems. However, one of the disadvantages of using frequency measurements 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 is 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 have compared the performance of three different inverse algorithms for delamination detection in the fibre-reinforced composite laminates: direct of solution using a graphical method, artificial neural network (ANN) and surrogate-based optimization. In particular, the graphical method which was earlier proposed for problems of two variables has been extended to solution of three variables, the interface, location along the beam length and size of delamination in laminated composite beams. The three inverse algorithms have been compared using numerical validation data generated from the theoretical model of delaminated beam with and without artificial errors. All three algorithms can predict the delamination parameters accurately using the validation data directly generated from theoretical model. However, if artificial errors are introduced in the numerical data to simulate uncertainties in measurement of frequencies, ANN does not fare as well as the the other two methods as it is more sensitive to the artificial discrepancies. Also, ANN requires the network to be retrained if the measured frequency modes do not match the input modes in the existing network. The graphical technique and the surrogate based optimization performed equally well in the validations. However, the graphical technique is only applicable to no more than three variables, while the surrogate-based optimization algorithm can be applied to inverse problems with several unknown parameters such as in the case of delaminations in composite plates.