A brief review of applications of ANN for NDT defect detection within the aeroplane industry
N Karimian and S Ghafouri Varzaneh
As with many industries and applications, in the aviation industry, safety is the most important factor. In order to comply with safety regulations and to further improve and guarantee the health of aircraft, continuous checks needs to be put in place in order to detect the presence of any defect or crack. Cracks and defects are typically formed due to the nature of employing the aircraft and aeroplanes in extreme weather or from the collision of birds with the surface. In doing so, non-destructive testing (NDT) methods as part of a quality control routine can be employed for this industry. Due to this reason, there is a large interest for the development of low-cost non-destructive inspection techniques that can be applied during normal routine tests, some of which employ artificial neural network (ANN) methods, which are popular and are generally employed in similar research fields. Many studies have been carried out regarding employing different algorithms of ANNs on such industry. This paper focuses on providing a brief review on the application of both ANN and NDT methods to detect the presence of defects and it also provides a comparison of these research outcomes that are employed.
