TY - CONF A1 - Bäßler, Ralph A1 - Yevtushenko, Oleksandra A1 - Le, Quynh Hoa A1 - Bettge, Dirk T1 - Suitability of Metallic Materials in CC(U)S Applications N2 - Carbon Capture Utilization and Storage (CCUS) is a promising technology to reach the target for reduction of CO2 emissions, e.g. from fossil-fuel operated power plants or cement mills. Crucial points for a sustainable and future-proof CCUS procedure are reliability and cost efficiency of the whole process chain, including separation of CO2 from the source, compression of CO2, its subsequent transportation to the injection site and injection into geological formations, e.g. aquifers. Most components that are in contact with CO2-stream consist of steel. Depending on the operating conditions (e.g. temperature, pressure, and CO2-stream composition) specific suitable steels should be used. The compressed CO2-stream is likely to contain process specific impurities; small amounts of SO2 and NO2 in combination with oxygen and water are most harmful. One approach, as currently preferred by pipeline operators, is to clean the CO2-stream to such levels, acceptable for carbon steel, commonly used as pipeline material. Another consideration would be, to use more corrosion resistant alloys for CO2-streams with higher amounts of impurities. Due to the absence of certified benchmarks for upper limits, systematic experiments with impurities in the CO2-stream were carried out reflecting mainly transport and injection conditions. Within the COORAL project (German acronym for “CO2 purity for capture and storage”) levels of impurities in the CO2-stream, being acceptable when using specific steels, were evaluated. Material exposure to dense or multiphase carbon dioxide (CO2) containing specific amounts of water vapor, oxygen (O2) sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) can be a challenge to steels. In some situations, condensation of impurities and reaction products from the CO2 stream can occur. CO2 saturated brine is supposed to rise in the well when the injection process is interrupted. The material selection shall ensure that neither CO2 nor brine or a combination of both will leak out of the inner tubing. This COORAL-work was extended by a follow-up project, called CLUSTER. Here the additional influence of impurities was investigated when merging CO2 streams from different sources, combined within a “so-called” cluster. Results are summarized within the following table regarding suitability for different parts of the process chain. T2 - EUROCORR 2021 CY - Online meeting DA - 20.09.2021 KW - Carbon capture storage KW - Corrosion KW - Steel KW - CCS KW - CCU KW - CO2 PY - 2021 SP - 1 EP - 2 AN - OPUS4-53460 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bäßler, Ralph A1 - Bettge, Dirk A1 - Bender, R. A1 - Féron, D. A1 - Mills, D. A1 - Ritter, S. A1 - de Graeve, I. A1 - Dugstad, A. A1 - Grassini, S. A1 - Hack, T. A1 - Halama, M. A1 - Han, E.-H. A1 - Harder, T. A1 - Hinds, G. A1 - Kittel, J. A1 - Krieg, R. A1 - Leygraf, C. A1 - Martinelli, L. A1 - Mol, A. A1 - Neff, D. A1 - Nilsson, J.-O. A1 - Odnevall, I. A1 - Paterson, S. A1 - Paul, S. A1 - Prosek, T. A1 - Raupach, M. A1 - Revilla, R. I. A1 - Ropital, F. A1 - Schweigart, H. A1 - Szala, E. A1 - Therryn, H. A1 - Tidblad, J. A1 - Virtanen, S. A1 - Volovitch, P. A1 - Watkinson, D. A1 - Wilms, M. A1 - Winning, G. A1 - Zheludkevich, M. T1 - La corrosion: un défi pour une société durable N2 - Une transition mondiale vers des systèmes énergétiques plus durables, abordables et fiables a été initiée par l’accord de Paris et l’Agenda 2030 des Nations unies pour un développement durable. Il s’agit là d’un défi industriel majeur car les systèmes et infrastructures énergétiques résilients au changement climatique exigent de se positionner pour le long terme. Se pencher sur le comportement dans la durée des matériaux structurels - principalement des métaux et des alliages - s’impose alors comme une nécessité. Dans cette optique, « La corrosion : un défi pour une société durable »présente une série de cas montrant l’importance de la tenue à la corrosion et de la protection anticorrosion des métaux et des alliages pour le développement de systèmes durables, économiques et fiables de production d’énergie. KW - Corrosion KW - Coûts de la corrosion KW - Protection KW - Anticorrosion KW - Stratégies deprévention PY - 2023 U6 - https://doi.org/10.51257/a-v1-cor2000 SN - 2555-5383 VL - 2023 IS - Avril SP - 1 EP - 23 CY - Saint-Denis, France AN - OPUS4-57927 LA - fra AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schmies, Lennart A1 - Botsch, B. A1 - Le, Q.-H. A1 - Yarysh, A. A1 - Sonntag, U. A1 - Hemmleb, M. A1 - Bettge, Dirk T1 - Classification of fracture characteristics and fracture mechanisms using deep learning and topography data T1 - Klassifikation von Bruchmerkmalen und Bruchmechanismen mittels Deep Learning und unter Verwendung von Topographiedaten N2 - In failure analysis, micro-fractographic analysis of fracture surfaces is usually performed based on practical knowledge which is gained from available studies, own comparative tests, from the literature, as well as online databases. Based on comparisons with already existing images, fracture mechanisms are determined qualitatively. These images are mostly two-dimensional and obtained by light optical and scanning electron imaging techniques. So far, quantitative assessments have been limited to macrocopically determined percentages of fracture types or to the manual measurement of fatigue striations, for example. Recently, more and more approaches relying on computer algorithms have been taken, with algorithms capable of finding and classifying differently structured fracture characteristics. For the Industrial Collective Research (Industrielle Gemeinschaftsforschung, IGF) project “iFrakto” presented in this paper, electron-optical images are obtained, from which topographic information is calculated. This topographic information is analyzed together with the conventional 2D images. Analytical algorithms and deep learning are used to analyze and evaluate fracture characteristics and are linked to information from a fractography database. The most important aim is to provide software aiding in the application of fractography for failure analysis. This paper will present some first results of the project. N2 - Die mikrofraktographische Analyse von Bruchflächen wird in der Schadensanalyse meist auf der Basis von Erfahrungswissen vorgenommen, welches aus vorliegenden Untersuchungen, eigenen Vergleichsversuchen und aus der Literatur und online Datenbanken stammt. Durch Vergleiche mit bereits vorliegenden Bildern werden qualitativ Bruchmechanismen ermittelt. Grundlage dafür sind zumeist zweidimensionale Aufnahmen aus licht- und elektronenoptischen Verfahren. Quantitative Aussagen beschränken sich bislang beispielsweise auf makroskopische Anteile von Bruchmechanismen oder die manuelle Ausmessung von Schwingstreifen. In jüngerer Zeit gibt es vermehrt Ansätze, Computer-Algorithmen einzusetzen, die in der Lage sind, unterschiedlich strukturierte Bruchmerkmale zu finden und zu klassifizieren. Im hier vorgestellten IGF-Vorhaben „iFrakto“ werden elektronenoptische Aufnahmen erzeugt und daraus Topographie-Informationen berechnet. Diese gewonnenen Topographie-Informationen werden zusammen mit den klassischen 2D-Bildern ausgewertet. Analytische Algorithmen und Deep Learning werden eingesetzt, um Bruchmerkmale zu analysieren, zu bewerten und mit Informationen aus einer fraktographischen Datenbank zu verknüpfen. Wichtigstes Ziel ist die Bereitstellung von Software zur Unterstützung der Fraktographie in der Schadensanalyse. In diesem Beitrag werden erste Ergebnisse des Vorhabens vorgestellt. KW - Fractography KW - Deep Learning KW - Classification PY - 2023 U6 - https://doi.org/10.1515/pm-2022-1008 SN - 0032-678X VL - 60 IS - 2 SP - 76 EP - 92 PB - De Gruyter AN - OPUS4-56962 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Le, Quynh Hoa A1 - Bäßler, Ralph A1 - Knauer, S A1 - Jaeger, P A1 - Kratzig, Andreas A1 - Bettge, Dirk A1 - Kranzmann, Axel T1 - Droplet corrosion of CO2 transport pipeline steels in simulated oxyfuel flue gas N2 - The research focus of this study was set on the corrosion process of condensate as droplets on the surface of carbon steels (X52, X70) martensitic steel UNS S41500, and super austenite UNS N08031 in CO2 atmosphere with impurities at 278 K (to simulate the offshore transportation condition in a buried pipeline). The possibility of dew/droplet formation on the steel surface and wetting behavior of corresponding materials were evaluated by contact angle measurement in dense CO2 at 278 K. To observe the effect of impurities (SO2 and O2) on droplet corrosion process, exposure tests were carried out in the mixed atmosphere with a drop, 1 ‑ 10 µL in volume, of CO2 saturated ultra-pure water on steel surface. Comparable exposure tests were carried out with the same gas mixture and the same volume of water, as vapor, to observe the droplet formation and the corrosion process that follows. Effects of surface roughness on the droplet formation and its corrosion process were further studied and showed no significant role upon long time exposure. The results from droplet experiments were compared to those from the bulk electrolyte for the further recommendation on the quality control of gas stream along with the use of carbon steels as transport pipelines in CCS - Carbon Capture and Storage system. KW - CCUS, supercritical/dense phase CO2, carbon steels, martensitic steel, superaustenite steel, droplet corrosion PY - 2018 UR - http://corrosionjournal.com/doi/abs/10.5006/2927 U6 - https://doi.org/10.5006/2927 SN - 0010-9312 SN - 1938-159X VL - 74 IS - 12 SP - 1406 EP - 1420 PB - NACE International CY - Houston, Texas, USA AN - OPUS4-46903 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bettge, Dirk A1 - Schmies, Lennart T1 - Die Fraktographische Online-Datenbank der AG Fraktographie – Entwicklungsstand und Planung T1 - The WG Fractography Online Database – Stage of Development and Planning N2 - Die AG Fraktographie im DVM/DGM-Gemeinschaftsgremium „Elektronenmikroskopie in der Materialforschung“ betreibt seit 2013 eine fraktographische online-Datenbank („FractoDB“), die für das interessierte Fachpublikum kostenfrei zur Verfügung steht. Die Analyse und Bewertung von Bruchflächen und der darauf befindlichen Bruchmerkmale ist ein wichtiger Teilaspekt der Schadensanalyse bei der Suche nach Schadensmechanismen und Schadensursachen. Risse und Brüche an realen Bauteilen können nur dann bewertet werden, wenn gut dokumentierte Vergleichsbrüche aus Laborversuchen zur Verfügung stehen, sei es an Proben oder an Vergleichs-Bauteilen. Daher trägt die AG Fraktographie Bildmaterial zusammen, führt systematisch Laborversuche und Ringversuche durch und analysiert Brüche aus Schadensfällen. Die gewonnenen Daten werden zu Datensätzen zusammengestellt und über die Datenbank zugänglich gemacht. Derzeit ist ein Bestand von über 400 Datensätzen mit insgesamt über 4.500 Bildern verfügbar, welcher durchsucht werden kann und in Anlehnung an die VDI 3822 organisiert ist. Weitere Aktivitäten der AG Fraktographie, die in der FractoDB abgebildet werden, sind u.a. die Entwicklung einer fraktographischen Symbolik und die Analyse von Bruchmerkmalen mittels Machine Learning. Über die aktuellen Ergebnisse und Planungen wird berichtet. N2 - Since 2013, the AG Fraktographie (Working Group (WG) Fractography) in the DVM/DGM Joint Committee “Elektronenmikroskopie in der Materialforschung” (Electron Microscopy in Materials Research) maintains a fractographic online database (“FractoDB”) available to interested professionals. When it comes to identifying failure mechanisms and causes of damage, the analysis and evaluation of fracture surfaces and their characteristics constitute important aspects of the failure analysis. Cracks and fractures in real components can only be assessed if well-documented comparative fractures from laboratory tests are available – be it in samples or in comparison components. The WG Fractography therefore gathers image material, systematically carries out laboratory and round robin tests, and analyzes fractures from damage cases. From the thus obtained data, datasets are compiled and made available via the database. Currently, a browsable inventory of more than 400 datasets with a total of more than 4500 images is available. It is organized in line with guideline VDI 3822. Other activities of the WG Fractography represented in the FractoDB include, amongst others, the development of a fractographic set of symbols and the analysis of fracture characteristics using machine learning. This contributbution reports on latest results and plans. KW - Fraktographie KW - Datenbank KW - Schadensanalyse KW - Bruchflächen KW - Machine Learning PY - 2023 U6 - https://doi.org/10.1515/pm-2023-0048 SN - 0032-678X VL - 60 IS - 9 SP - 569 EP - 579 PB - De Gruyter CY - Berlin AN - OPUS4-58200 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schmies, Lennart A1 - Hemmleb, Matthias A1 - Bettge, Dirk T1 - Relevant input data for crack feature segmentation with deep learning on SEM imagery and topography data N2 - Fractography plays a critical role in failure analysis of engineering components and has a considerable importance for safety investigations. Usually, the interpretation of fracture surfaces is done by experts with the help of literature and experimental data, that requires a lot of experience. The use of deep learning (DL) with neural networks in failure analysis becomes more and more relevant with the rapidly developing possibilities. Especially, the modern network architectures can assist fractographers in determining various fracture features on SEM images of the fracture surfaces. The basis for the best possible evaluation is the understanding of the influence of the input data used for training deep neural networks (DNN). Therefore, this study discusses the influence of the selection of the input data used for the prediction quality of these networks in order to take this into account for future data acquisition. Specimens of various metallic materials were subjected to fatigue cracking experiment under laboratory conditions. The fractured surfaces were then imaged using various modes or detectors (such as SE, BSE and topography) in SEM, and those captured images were used to create a training data set. The relevance of the individual data for the quality of the prediction is determined by a specific combination of the different detector data. For the training, the well-established architecture of a UNet-ResNet34 with a fixed set of hyperparameters is used. It has been found in this present study that the combination of all input data significantly increases the prediction accuracy, whereby even the combination of SE and BSE data provides considerable advantages over the exclusive use of SE images. KW - Fractography KW - Machine Learning KW - Deep Learning KW - KI PY - 2023 U6 - https://doi.org/10.1016/j.engfailanal.2023.107814 VL - 156 SP - 1 EP - 8 PB - Elsevier AN - OPUS4-58918 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bettge, Dirk A1 - Le, Quynh-Hoa A1 - Roth, J. T1 - Entwicklung einer fraktographischen Symbolik N2 - Bei der Untersuchung von Bruchflächen werden zunächst Beobachtungen gemacht, d.h. makroskopische und mikroskopische Merkmale analysiert und in Bildern dokumentiert. Im zweiten Schritt werden anhand der gefundenen Merkmale Rissverlauf und Bruchmechanismen ermittelt. Abschließend sollen diese Ergebnisse in einem Bericht oder Gutachten zusammengefasst werden. Hierbei ist eine skizzenhafte Darstellung der fraktographischen Befunde hilfreich für das Verständnis des Lesers. Ein vorgefertigter fraktographischer Symbolsatz würde dies erheblich vereinfachen. Skizzen von fraktographischen Befunden sind in der Literatur recht häufig zu finden, z.B. in der VDI 3822 oder dem ASM Handbook Fractography, allerdings gibt es hierzu keine umfassende Systematik, wie sie z.B. aus der Meteorologie (Wetterkarten), Geographie (Landkarten, Seekarten) oder Geologie (Bodenkarten) seit langer Zeit bekannt sind. Deshalb wird hier der Versuch unternommen, einen Symbolsatz zu entwickeln und zur Verfügung zu stellen, mit dessen Hilfe fraktographische Befunde in eindeutiger und übersichtlicher Weise veranschaulicht werden können. Der fraktographische Symbolsatz ("Fracto Graphics") befindet sich in Entwicklung, ist offen zur Diskussion sowie für Verbesserungen. T2 - 55. Metallographie-Tagung CY - Online meeting DA - 29.9.2021 KW - Fraktographie KW - Symbolik KW - Schadensanalyse PY - 2021 AN - OPUS4-53433 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bettge, Dirk A1 - Le, Quynh-Hoa A1 - Yarysh, Anna T1 - MGA Round Robin Test on Al-AM Fatigue Testing - Fractographic Results N2 - Presentation of results of an investigation of fracture mechanisms and crack start sites of an additive manufactured aluminium alloy after fatigue testing. Collaboration within the MGA initiative (Mobility Goes Additive). T2 - MGA Mid Term Meeting 2022 CY - Berlin, Germany DA - 05.07.2022 KW - Aluminium Alloy KW - Fractography KW - Additive Manufacturing PY - 2022 AN - OPUS4-55192 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lemiasheuski, Anton A1 - Bajer, Evgenia A1 - Oder, Gabriele A1 - Göbel, Artur A1 - Hesse, Rene A1 - Bettge, Dirk T1 - Development of an Automated 3D Metallography System (RASI) and its Application in Microstructure Analysis N2 - Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen. Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation. T2 - Euromat 2023 CY - Frankfurt a. M., Germany DA - 04.07.2023 KW - Metallography KW - 3D Reconstruction KW - Roboter KW - Automation KW - Microstructure PY - 2023 AN - OPUS4-58202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bettge, Dirk A1 - Schmies, Lennart T1 - Die Fraktographische Online-Datenbank der AG Fraktographie – Entwicklungsstand und weitere Planung N2 - Vortrag zum aktuellen Stand der fraktographischen online-Datenbank. Darstellung des AGM/DVM Gemeinschaftsausschusses REM in der Materialforschung und seiner Arbeitsgruppen Fraktographie, EBSD und 2D/3D-Prüfung. Ziele und Inhalte der fraktographischen Datenbank, Aufruf zum Mitmachen. Durchführung von Vergleichsversuchen, Entwicklung einer fraktographischen Symbolik, Ringversuch, Vorhaben iFrakto mit Entwicklung einer KI-gestützten Bruchflächenanalyse. T2 - Metallographietagung 2023 CY - Leoben, Austria DA - 13.09.2023 KW - Fraktographie KW - Machine Learning KW - Datenbank PY - 2023 AN - OPUS4-58467 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -