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In-line laser thermography for crack detection at elevated temperature: A Numerical modeling study
(2016)
The detection and characterization of cracks prior to damage is a technologically and economically highly significant task and is of very importance when it comes to safety-relevant structures. The evaluation of a components life is closely related to the presence of cracks in it. Laser thermography has already high capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples. Crack detection in metallic samples at high temperature is highly significant in present manufacturing scenario. During the casting process of billets, surface cracks form, due to the suboptimal cooling rates. These cracks reduce value of the billet and must be removed using machining process after cooling. This secondary process increases cost of manufacturing. In this work we developed a heat transfer model for laser thermography to study the thermal contrast variation with increase in surface temperature using finite element method (FEM). Here we are mainly concentrating the capability of the scanning laser thermography in crack detection which are in elevated temperature and numerical modeling study of thermal contrast variation of crack with respect increase in metal surface temperature. This study is important to prove the capability of laser thermography for crack detection in elevated temperature. Since we are using High power CW Laser to local heating of the metal surface which can give relatively high thermal contrast even at elevated temperature compare to other heating source. Here we are modeled and simulated 2D laser scanning across a surface breaking crack and developed an algorithm to produce the vicinity of crack. The algorithm we developed applied for various surface temperature data. And validated the credibility of the algorithm with experimental data.
Using an infrared camera for radiometric imaging allows the contactless temperature measurement of multiple surface pixels simultaneously. From the measured surface data, a sub-surface structure, embedded inside a sample or tissue, can be reconstructed and imaged when heated by an excitation light pulse. The main drawback in radiometric imaging is the degradation of the spatial resolution with increasing depth, which results in blurred images for deeper lying structures. We circumvent this degradation with blind structured illumination, combined with a non-linear joint sparsity reconstruction algorithm. The ground-breaking concept of super-resolution can be transferred from optics to thermographic imaging.