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Description: In civil engineering, the corrosion of steel reinforcements in structural elements of concrete bares a risk of stability-reduction, mainly caused by the exposure to chlorides. 3D computed tomography (CT) reveals the inner structure of concrete and allows one to investigate the corrosion with non-destructive testing methods. To carry out such investigations, specimens with a large artificial crack and an embedded steel rebar have been manufactured. 3D CT images of those specimens were acquired in the original state. Subsequently three cycles of electrochemical pre-damaging together with CT imaging were applied. These time series have been evaluated by means of image processing algorithms to segment and quantify the corrosion products. Visualization of the results supports the understanding of how corrosion propagates into cracks and pores. Furthermore, pitting of structural elements can be seen without dismantling. In this work, several image processing and visualization techniques are presented that have turned out to be particularly effective for the visualization and segmentation of corrosion products. Their combination to a workflow for corrosion analysis is the main contribution of this work.

Description: Monocrystaline Ni-base superalloys are the material of choice for first row blades in jet engine gas turbines. Using a novel visualization tool for 3D reconstruction and visualization of dislocation line segments from stereo-pairs of scanning transmission electron microscopies, the superdislocation substructures in Ni-base superalloy LEK 94 (crept to ε = 26%) are characterized. Probable scenarios are discussed, how these dislocation substructures form.

Description: Die Auswertungen der großen Datenmengen moderner bildgebender Verfahren der ZfP können manuell kaum noch bewältigt werden. Hochauflösende 3D-CT-Aufnahmen bestehen oft aus über 1000 Schichtbildern mit einer Datenmenge von mehreren Gigabytes. Aktuelle Computer können diese zwar problemlos visualisieren und erlauben somit eine visuelle Inspektion, aber die möglichst vollständige Erkennung bestimmter Merkmale in den Daten und deren qualitative wie quantitative Auswertung ist durch Experten manuell nicht mehr zu bewältigen. Das gilt insbesondere im Kontext der Schadensaufklärung für die quantitative Analyse verschiedenartig induzierter Risse in Betonen (z.B. durch mechanische Belastungen sowie Frost, Sulfat und Alkali-Kieselsäure-Reaktion). Eine dazu notwendige Segmentierung und Merkmalserkennung kann nur automatisch durchgeführt werden. Dabei ergibt sich (auch fast automatisch) die Frage nach der Verlässlichkeit der verwendeten Algorithmen. Inwieweit kann man davon ausgehen, dass alle gesuchten Merkmale auch tatsächlich gefunden worden sind? Sind die gefundenen Merkmale quantitativ auswertbar und wie wirken sich Parameteränderungen auf die Ergebnisse aus? Sollten immer dieselben, einmal mit gutem Ergebnis angewandten, Parameter auch bei anderen Proben zur besseren Vergleichbarkeit genutzt werden? Anhand eines Risserkennungsalgorithmus basierend auf Formerkennung und Bildverarbeitung wird die Problematik diskutiert. Als Grundlage zur Bewertung des Ansatzes dienen 3D-CT-Aufnahmen von geschädigten Betonprobekörpern und Datensätze, in denen sich aufgrund ihres homogenen Aufbaus mit einfachen Mitteln Risse sicher und eindeutig erkennen lassen. Zur Auswertung der erkannten Risse gehört auch deren Einbettung in das umliegende Material. Das erfordert neben einer automatischen Risserkennung auch eine Segmentierung des gesamten Probekörpers in Zementsteinmatrix, Gesteinskörnung und Porenraum. Da eine solche Segmentierung aufgrund der Datenmenge nur schwer manuell erfolgen kann, werden erste Ergebnisse aus einer Segmentierung mit Hilfe selbstlernender Convolutional Neural Networks gezeigt.

Description: A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo-pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo-pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (〉 300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo-pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3 % and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed.

Description: A great amount of material properties is strongly influenced by dislocations, the carriers of plastic deformation. It is therefore paramount to have appropriate tools to quantify dislocation substructures with regard to their features, e.g., dislocation density, Burgers vectors or line direction. While the transmission electron microscope (TEM) has been the most widely-used equipment implemented to investigate dislocations, it usually is limited to the two-dimensional (2D) observation of three-dimensional (3D) structures. We reconstruct, visualize and quantify 3D dislocation substructure models from only two TEM images (stereo pairs) and assess the results. The reconstruction is based on the manual interactive tracing of filiform objects on both images of the stereo pair. The reconstruction and quantification method are demonstrated on dark field (DF) scanning (S)TEM micrographs of dislocation substructures imaged under diffraction contrast conditions. For this purpose, thick regions (〉300 nm) of TEM foils are analyzed, which are extracted from a Ni-base superalloy single crystal after high temperature creep deformation. It is shown how the method allows 3D quantification from stereo pairs in a wide range of tilt conditions, achieving line length and orientation uncertainties of 3% and 7°, respectively. Parameters that affect the quality of such reconstructions are discussed.

Description: To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.

Description: To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too. [1.2MB | id=23664 ] iCT 2019 Session: Short talks Thu 13:50 Auditorium2019-03 Möglichkeiten und Grenzen automatischer Merkmalserkennung am Beispiel von Risserkennungen in 3D-CT-Aufnahmen von Betonproben O. Paetsch11 Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany Abstract [1MB | id=23104 ] DE DGZfP 2018 Session: Bauwesen2018-09 Quantitative Rissanalyse im Fahrbahndeckenbeton mit der 3D-Computertomographie D. Meinel125, K. Ehrig128, F. Weise16, O. Paetsch211 1Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany concrete, ROI tomography, in-situ-CT, 3D-CT, Beton, AKR, Feuchtetransport, automatic crack detection Abstract [0.7MB | id=18980 ] DE DGZfP 2015 Session: CT Algorithmen2016-04 3D Corrosion Detection in Time-dependent CT Images of Concrete O. Paetsch111, D. Baum15, S. Prohaska17, K. Ehrig228, D. Meinel225, G. Ebell24 1Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany CT, multi-angle radiography, defect detection, Feature Extraction, image processing, concrete, corrosion Abstract [0.5MB | id=18043 ] DIR 2015 Session: Quantitative imaging and image processing2015-08 Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben O. Paetsch111, D. Baum15, G. Ebell24, K. Ehrig228, A. Heyn2, D. Meinel225, S. Prohaska17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Computertomographie [0.4MB | id=17375 ] DE DGZfP 2014 Session: Bauwesen2015-03 Examination of Damage Processes in Concrete with CT D. Meinel125, K. Ehrig128, V. L’Hostis2, B. Muzeau2, O. Paetsch311 1BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Laboratoire d’Etude du Comportement des Bétons et des Argiles; Commissariat Energie Atomique (CEA)287, Gif-Sur-Yvette, France 3Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany X-ray computed tomography, concrete, corrosion, crack detection, 3D visualization Abstract [4.9MB | id=15692 ] iCT 2014 Session: Non-destructive Testing and 3D Materials Characterisation of...2014-06 3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben O. Paetsch111, D. Baum15, D. Breßler1, K. Ehrig228, D. Meinel225, S. Prohaska1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Radiographic Testing (RT), statistical analysis, 3D Computed Tomography, visualization, concrete structural damage, automated crack detection [1MB | id=15343 ] DE DGZfP 2013 Session: Computertomographie2014-03 Vergleich automatischer 3D-Risserkennungsmethoden für die quantitative Analyse der Schadensentwicklung in Betonproben mit Computertomographie O. Paetsch111, K. Ehrig228, D. Meinel225, D. Baum15, S. Prohaska1,1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany Radiographic Testing (RT), visualization, crack detection, Visualisierung, computer tomography, template matching, Hessian eigenvalues, ZIBAmira, automated crack detection, percolation [0.9MB | id=14269 ] DE DGZfP 2012 Session: Computertomographie2013-05 Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography O. Paetsch111, D. Baum15, K. Ehrig228, D. Meinel225, S. Prohaska1,1,17 1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany computed tomography, template matching, Hessian eigenvalues, crack statistics, visualization, crack surface, ZIBAmira [0.6MB | id=13736 ] iCT 2012 Session: Poster - Analysis and Algorithms2012-12 3-D-Visualisierung von Radar- und Ultraschallecho-Daten mit ZIBAmira D. Streicher112, O. Paetsch211, R. Seiler2, S. Prohaska27, M. Krause360 [Profile of Krause] , C. Boller178 1Saarland University74, Saarbrücken, Germany 2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany 3BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany [0.4MB | id=12284 ] DE DGZfP 2011 Session: Bauwesen2012-05 Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography K. Ehrig128, J. Goebbels153, D. Meinel125, O. Paetsch211, S. Prohaska27, V. Zobel2 1Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany 2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany [0.7MB | id=11150 ] DIR 2011 Session: Poster2011-11 Actual Cooperations 10th International Workshop NDT in Progress 2019 2019 Oct 7-9 11th International Symposium on NDT in Aerospace 2019 2019 Nov 13-15 3rd Singapore International NDT Conference & Exhibition, SINCE 2019 2019 Dec 4-5 10th Conference on Industrial Computed Tomography (iCT) 2020 2020 Feb 4-7 34th European Conference on Acoustic Emission Testing (EWGAE 2020) 2020 Sep 9-11 Contribute Papers and Proceedings to NDT.net Share... Home Exhibition Archive Forum Jobs Members Events Directory NDT A-Z Advertise Privacy Policy Contact About © NDT.net - Where expertise comes together. The Largest Open Access Portal of Nondestructive Testing (NDT)- since 1996