A state-of-the-art review of non-destructive testing image fusion and critical insights on the inspection of aerospace composites towards sustainable maintenance repair operations

Non-destructive testing (NDT) of aerospace structures has gained significant interest, given its non-destructive and economic inspection nature enabling future sustainable aerospace maintenance repair operations (MROs). NDT has been applied to many different domains, and there is a number of such methods having their individual sensor technology characteristics, working principles, pros and cons. Increasingly, NDT approaches have been investigated alongside the use of data fusion with the aim of combining sensing information for improved inspection performance and more informative structural health condition outcomes for the relevant structure. Within this context, image fusion has been a particular focus. This review paper aims to provide a comprehensive survey of the recent progress and development trends in NDT-based image fusion. A particular aspect included in this work is providing critical insights on the reliable inspection of aerospace composites, given the weight-saving potential and superior mechanical properties of composites for use in aerospace structures and support for airworthiness. As the integration of NDT approaches for composite materials is rather limited in the current literature, some examples from non-composite materials are also presented as a means of providing insights into the fusion potential

Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine Learning Classification

Composite materials are one of the primary structural components in most current transportation applications, such as the aerospace industry. Composite material diagnostics is a promising area in the fight against structural damage in aircraft and spaceships. Detection and diagnostic technologies often provide analysts with a valuable and rapid mechanism to monitor the health and safety of composite materials. Although many attempts have been made to develop damage detection techniques and make operations more efficient, there is still a need to develop/improve existing methods. Pulsed thermography (PT) technology was used in this study to obtain healthy and defective data sets from custom-designed composite samples having similar dimensions but different thicknesses (1.6 and 3.8). Ten carbon fibre-reinforced plastic (CFRP) panels were tested. The samples were subjected to impact damage of various energy levels, ranging from 4 to 12 J. Two different methods have been applied to detect and classify the damage to the composite structures. The first applied method is the statistical analysis, where seven different statistical criteria have been calculated. The final results have proved the possibility of detecting the damaged area in most cases. However, for a more accurate detection technique, a machine learning method was applied to thermal images; specifically, the Cube Support Vector Machine (SVM) algorithm was selected. The prediction accuracy of the proposed classification models was calculated within a confusion matrix based on the dataset patterns representing the healthy and defective areas. The classification results ranged from 78.7% to 93.5%, and these promising results are paving the way to develop an automated model to efficiently evaluate the damage to composite materials based on the non-distractive testing (NDT) technique