Structural health monitoring is the process of acquisition and analyzing technical data obtained from structures to determine the present condition of the structure and residual life. Composites have been widely in use because of their low weight and better mechanical properties compared to conventional metals. They are more prone to damage during cyclic loading and the impact of foreign objects. So, usage of the nondestructive techniques is important to detect such damage in composites at the beginning stage itself, which further helps to avoid catastrophic failure. Many review articles are discussing a single nondestructive technique to monitor the health of the structure, but a single technique is not sufficient in most of the cases. This review is focused on the most commonly used nondestructive health monitoring techniques such as acoustic emission, vibration testing, ultrasonic testing, infrared thermography, and shearography to detect and characterize the damage in composite structures used in aerospace, automotive, and marine applications. The comparison among the techniques also has been presented in this review.
Delamination is definitely an important topic in the area of composite structures as it progressively worsens the mechanical performance of fiber-reinforced polymer composite structures in its service period. The detection and severity analysis of delaminations in engineering areas like the aviation industry is vital for safety and economic considerations. The existence of delaminations varies the vibration characteristics such as natural frequencies, mode shapes, etc. of composites and hence this indication can be effectively used for locating and quantifying the delaminations. The changes in vibration characteristics are considered as inputs for the inverse problem to determine the location and size of delaminations. In this paper Artificial Neural Network (ANN) is used for delamination evaluationof glass fiber-reinforced composite beams using natural frequency as typical vibration parameter. The Finite Element Analysis is used for generating the required dataset for ANN. The frequency-based delamination prediction technique is validated by finite element models and experimental modal analysis. The results indicate that the ANN-based back propagation algorithm can predict the location and size of delaminations in composites with good accuracy for numerical natural frequency data but the accuracy is comparitivelyless for experimental natural frequency data.
The importance of delamination detection can be understood from aircraft components like Vertical Stabilizer, which is subjected to heavy vibration during the flight movement and it may lead to delamination and finally even flight crash can happen because of that. Any solid structure's vibration behaviour discloses specific dynamic characteristics and property parameters of that structure. This research investigates the detection of delamination in composites using a method based on vibration signals. The composite material's flexural stiffness and strength are reduced as a result of delaminations, and vibration properties such as natural frequency responses are altered. In inverse problems involving vibration response, the response signals such as natural frequencies are utilized to find the location and magnitude of delaminations. For different delaminated beams with varying position and size, inverse approaches such as Response Surface Methodology (RSM) and Artificial Neural Network (ANN) are utilized to address the inverse problem, which aids in the prediction of delamination size and location.
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