Validation of algorithms for delamination detection in composite structures using experimental data

Abstract

This research work deals with aspects concerned with delamination detection in composite structures as revealed by an approach based on vibration measurements. Variations in vibration characteristics generated in composite laminates indicate the existence of delaminations because degradation due to delamination causes reduction in flexural stiffness and strength of the material and as a result vibration parameters like natural frequency responses are changed. Hence, it is possible to monitor the variation in natural frequencies to identify the presence of delamination, and assess its size and location for online structural health monitoring (SHM). The approach to this paper, therefore, typically depends on undertaking the analysis of structural models implemented by finite element analysis (FEA). The numerical solutions using FE models known as the simulator computes the natural frequencies for the delaminated and undelaminated specimens of composite laminates. However, these FE models are computationally expensive, and surrogate (approximation) models are introduced to curtail the computational expense. The simulator is employed to solve the inverse problem using algorithms based on computational intelligence concepts. An artificial neural network (ANN) model is developed to also solve the inverse problem for delamination detection directly and to provide surrogate models integrated with optimization algorithms (the gradient-based local search and non-dominated sorting genetic algorithm-II) to contain the computationally expensive simulations by FEA. This approach is termed as surrogate assisted optimization and it is seen that the engagement of surrogate models in lieu of the FE models in the optimization loop greatly enhances the accuracy of delamination detection results within an affordable computational cost and provides control over handling different variables. Meanwhile, to aid with the building of effective surrogate models using substantial number of training datasets, K-means clustering algorithm is harnessed and this effectively reduces the large training datasets usually required for ANN training. This paper demonstrated that ANN and optimization algorithms with surrogates show immense potentialities for use in delamination damage detection scenarios. Prediction errors of the algorithms were quantified and they were shown to be satisfactory when applied to previously experimental data. The algorithms in their inverse formulations are capable of predicting accurately delamination parameters. Hence, these algorithms should be employed for application in the domain of SHM where their small computational requirements could be exploited for online damage detection.

Vibration-based delamination detection in composite beams through frequency changes

“…The full assessment of the damage requires the solution of the inverse problem, i.e., determination of the location and severity of damage from measured changes in multiple modal frequencies. Previous researchers have mainly resorted to advanced system identification or artificial intelligence such as neural networks (Chakraborty, 2005;Okafor et al, 1996;Valoor and Chandrashekhara, 2000;Islam and Craig, 1994;Wei et al, 2005) and genetic algorithm (Su et al, 2005;Ihesiulor et al, 2012). However, when only two or three parameters are to be determined, such as location along the length of the beam, the depth at which it occurs and the size of the delamination, it can be easily determined using a graphical technique (Zhang et al, 2011).…”

RSM ve Grey Wolf Optimizasyonu Kullanılarak Enjeksiyon Kalıplanmış PA66+PA6I/6T Kompozitinin Mekanik Özelliklerinin Deneysel Olarak İncelenmesi

“…It is especially important to minimize warpage, shrinkage, and residual stress which reduces the performance of the final product after the addition of fiber to this polymer in the plastic injection production method. Because, the addition of fiber directly affects the polymer's properties and injection molding parameters as well as the strength, quality, and manufacturing cost of product [1,2,3]. On the other hand, since the plastic injection molding is a nonlinear phenomenon, it is difficult to determine the optimum process parameters.…”

Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

“…Two research presented by Ihesiulor et al. 21,22 about delamination detection with error and noise polluted natural frequencies using computational intelligence concepts and validation of algorithms for delamination detection using experimental data. Thalapil and Maiti 23 analyzed the natural vibration of monolithic beams with longitudinal cracks for developing a method for its detection.…”