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- Crack Detection (3) (entfernen)
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In this work, which is part of a larger research program, a framework called “virtual data fusion“ was developed to provide an automated and consistent crack detection method that allows for the cross-comparison of results from large quantities of X-ray Computed Tomography (CT) data. A partial implementation of this method in a custom program was developed for use in research focused on crack quantification in Alkali-Silica Reaction (ASR)-sensitive concrete aggregates. During the CT image processing, a series of image analyses tailored for detecting specific, individual crack-like characteristics were completed. The results of these analyses were then “fused” in order to identify crack-like objects within the images with much higher accuracy than that yielded by any individual image analysis procedure. The results of this strategy demonstrated the success of the program in effectively identifying crack-like structures and quantifying characteristics, such as surface area and volume. The results demonstrated that the source of aggregate has a very significant impact on the amount of internal cracking, even when the mineralogical characteristics remain very similar. River gravels, for instance, were found to contain significantly higher levels of internal cracking than quarried stone aggregates of the same mineralogical type.
This poster provides an overview of ultrasonic investigations of an engineered test barrier at ERAM Morsleben, which were completed as part of contractual work with the Bundesgesellschaft für Endlagerung mbH (BGE). This includes both experiments with the Large Aperture Ultrasound System (LAUS) and the ultrasonic borehole array. Also included in the poster is a description of the planned BAM thematic project “SealWasteSafe”. In particular, the proposed geopolymer materials are described and future research requirements are detailed.
The aim of this work is to illustrate the contribution of signal processing techniques in the field of Non-Destructive Evaluation. A component’s life evaluation is inevitably related to the presence of flaws in it. The detection and characterization of cracks prior to damage is a technologically and economically significant task and is of very importance when it comes to safety-relevant measures. The Laser Thermography is the most effective and advanced thermography method for Non-Destructive Evaluation. High capability for the detection of surface cracks and for the characterization of the geometry of artificial surface flaws in metallic samples of laser thermography is particularly encouraging. This is one of the non- contacting, fast and real time detection method. The presence of a vertical surface breaking crack will disturb the thermal footprint. The data processing method plays vital role in fast detection of the surface and sub-surface cracks.
Currently in laser thermographic inspection lacks a compromising data processing algorithm which is necessary for the fast crack detection and also the analysis of data is done as part of post processing. In this work we introduced a raw data based image processing algorithm which results precise, better and fast crack detection. The algorithm we developed gives better results in both experimental and modeling data. By applying this algorithm we carried out a detailed investigation Variation of thermal contrast with crack parameters like depth and width. The algorithm we developed is applied for various surface temperature data from the 2D scanning model and also validated credibility of algorithm with experimental data.