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Laser-based active thermography is a contactless non-destructive testing method to detect material defects by heating the object and measuring its temperature increase with an infrared camera. Systematic deviations from predicted behavior provide insight into the inner structure of the object. However, its resolution in resolving internal structures is limited due to the diffusive nature of heat diffusion. Thermographic super resolution (SR) methods aim to overcome this limitation by combining multiple thermographic measurements and mathematical optimization algorithms to improve the defect reconstruction.
Thermographic SR reconstruction methods involve measuring the temperature change in an object under test (OuT) heated with multiple different spatially structured illuminations. Subsequently, these measurements are inputted into a severely ill-posed and heavily regularized inverse problem, producing a sparse map of the OuT’s internal defect structure. Solving this inverse problem relies on limited priors, such as defect-sparsity, and iterative numerical minimization techniques. Previously mostly experimentally limited to one-dimensional regions of interest (ROIs), this thesis aims to extend the method to the reconstruction of two-dimensionalROIs with arbitrary defect distributions while maintaining reasonable experimental complexity. Ultimately, the goal of this thesis is to make the method suitable for a technology transfer to industrial applications by advancing its technology readiness level (TRL).
In order to achieve the aforementioned goal, this thesis discusses the numerical expansion of a thermographic SR reconstruction method and introduces two novel algorithms to invert the underlying inverse problem. Furthermore, a forward solution to the inverse problem in terms of the applied SR reconstruction model is set up. In conjunction with an additionally proposed algorithm for the automated determination of a set of (optimal) regularization parameters, both create the possibility to conduct analytical simulations to characterize the influence of the experimental parameters on the achievable reconstruction quality. On the experimental side, the method is upgraded to deal with two-dimensional ROIs, and multiple measurement campaigns are performed to validate the proposed inversion algorithms, forward solution and two exemplary analytical studies. For the experimental implementation of the method, the use of a laser-coupled DLP-projector is introduced, which allows projecting binary pixel
patterns that cover the whole ROI, reducing the number of necessary measurements per ROI significantly (up to 20x).
Finally, the achieved reconstruction of the internal defect structure of a purpose-made OuT is qualitatively and qualitatively benchmarked against well-established thermographic testing methods based on homogeneous illumination of the ROI. Here, the background-noise-free two-dimensional photothermal SR reconstruction results show to outclass all defect reconstructions by the considered reference methods.
The aim of this presentation is to highlight how psychology can be used to prevent human error. It starts with examples of accidents, incidents and events that have happened due to too little attention being dedicated to human factors. It continues arguing that human factors are one of the main factors influencing the reliability of non-destructive testing and gives definitions of the main terms. Furthermore, it presents a method used to identify risks in mechanized NDT to be used for the purposes of the final disposal of spent nuclear fuel and presents a study, in which human-centred design and eye tracking have been used to optimism the inspection procedure. The conclusion is that human factors methods can be used to identify problems during the inspection process and generate mitigation strategies that can be used to decrease human error and enhance safety.
Influence of the real energy input on the sensitivity of thermographic testing in case of GFRP
(2018)
Thermographic testing (TT) is an upcoming nondestructive method, which requires no contact at all to the specimen and can be applied on larger areas simultaneously. The measurement concept is based on the production of a thermal imbalance at the surface of the object under test. When the surface of this object is heated by an external source for a certain time, the surface temperature drops subsequently, influenced by inner defects of the sample. This leads to thermal contrasts at the surface. It is crucial that those contrasts are large enough to be detectable above the noise level. In a first approximation, the observed temperature contrast at a defect is proportional to the energy which was really introduced into the specimen during the heating period. However, the real energy input in a TT experiment is almost always unknown due to distinct parameters of the experimental setup or the material investigated. Typically, only the power consumption of the heating sources is reported, sometimes combined with the distance to the specimen surface.
This contribution describes the thermographic inspection of a rear side thickness variation from 1 to 2 cm at GFRP. This could represent a rear side adhesive bond i.e. in a wind turbine rotor blade. The front side heating was realized by usual halogen lamps with variable radiation power. The detected temperature contrast at the front side will be related to the different energy inputs determined by means of a simple analytical model applied to the experimental data. Additionally, the experimental data are compared with results of FEM simulations performed by COMSOL Multiphysics.
The results clearly demonstrate the key role of the real energy input in a real TT setup, if detection limits have to be evaluated.