Optimization of line scanning thermography of composite materials for aerospace industry using advanced modeling and analysis algorithms
|Abstract:||In the last decade, emerging of advanced materials and manufacturing methods leads to produce the mechanical components, which provide better mechanical specifications with lower weight. These special products are used in the high technology industries such as aerospace and military. Hence, the product quality is vital to achieve a secure product. Non-destructive Testing (NDT) is one of the popular methods, which is employed to detect the internal defects of different materials. This technique does not have any negative effect on the specimens. The various techniques of nondestructive testing are used in different fields to ensure accuracy, verify integrity, reduce production costs and detect defects. Various NDT methods were introduced and developed to detect the flaws and delamination which have been used according to defect size and type, material, and defect location. Line scan thermography (LST) is a dynamic thermography technique, which is used to inspect large components of metallic surfaces and composites, commonly used in the aerospace industry. As a nondestructive testing and evaluation (NDT&E) technique, LST is a dynamic technique suited to inspect large and complex aerospace components. The robotized LST method provides advantages in comparison to the static approaches. Robotized LST provides heating uniformity and allows image processing which enhances the detection probability, allowing a large-scale component to be inspected without the loss of resolution. Using the LST approach, it is possible to inspect large areas at high scan speeds. Also, the inspection results are immediately available for analysis while the scanning process continues. One of the important challenges in LST method is the number of parameters such as scanning speed, power, the distance between source and specimen, which affect the LST performance. The optimal values are dependent on the material structure, thermal specifications of the composite material, defect shape and infrared camera resolution. In order to determine the optimal parameters, the LST is simulated using a 3D finite element method (FEM). The main objective of this thesis is to maximize the detection depth and the signal-to-noise (SNR) value at maximum signal contrast as the criteria to evaluate the inspection quality and performance. A composition of the analytical model of LST thermography, 3D finite element approach and experimental data is employed to find the optimal LST parameters. The signal processing techniques that were initially developed to be applied on pulse thermography have been successfully implemented to enhance the detection probability.|
|Document Type:||Thèse de doctorat|
|Open Access Date:||27 September 2018|
|Collection:||Thèses et mémoires|
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