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 DO - 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 - 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 DO - 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 DO - 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 - 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 - Le, Quynh Hoa A1 - Bäßler, Ralph A1 - Knauer, S A1 - Kratzig, Andreas A1 - Bettge, Dirk A1 - Kranzmann, Axel T1 - Droplet corrosion of CO2 transport pipeline steels N2 - This work examined the droplet corrosion of CO2 pipeline steels caused by impurities in CO2 supercritical/dense phase at 278 K, simulating the underground transport condition. The wetting properties of carbon steels (X52 and X70) as well as martensitic steel UNS S41500, and superaustenite UNS N08031 were studied by contact angle measurement, revealing reactive wetting behavior of carbon steels. Exposure tests with CO2 saturated water droplet on steel surface showed that the impurities (220 ppmv SO2 and 6700 ppmv O2) diffused into the droplet and then reacted with metal coupons in supercritical/dense phase condition, forming the corrosion product instantly during pumping process. Due to the active wetting behavior, the carbon steels suffered from heavily attack, while negligible corrosion product was observed in cases of martensitic steel UNS S41500 and superaustenite UNS 08031 coupons. Condensation experiments that were carried out on fresh polished coupons in CO2 with 1200 ppmv H2O showed that the formation and aggregation of droplet is dependent on the presence of impurities. Without SO2 and O2, the same concentration of H2O did not cause observable corrosion process after a week of exposure. With 220 ppmv SO2 and 6700 ppmv O2 even low water concentration (5-30 ppmv) still resulted in heterogeneous nucleation and subsequent growth of droplets, leading to corrosive process on carbon steel surface albeit to a lesser extent. T2 - CORROSION 2018 CY - Phoenix, AZ, USA DA - 15.04.2018 KW - CCUS KW - Supercritical/dense phase CO2 KW - Carbon steels KW - Martensitic steel KW - Superaustenite steel KW - Droplet corrosion PY - 2018 SP - Paper 10845, 1 EP - 11 PB - NACE International Publications Division CY - Houston, Texas, USA AN - OPUS4-44798 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kratzig, Andreas A1 - Bettge, Dirk A1 - Le, Quynh Hoa A1 - Bäßler, Ralph A1 - Kranzmann, Axel T1 - Interaction of Oxidizing and Reductive Components in CO2 Streams with Transport Pipeline Steel X70 at High Pressure and Low Temperature N2 - Specific amounts of oxidizing and reductive impurities as well as some moisture were added to dense phase CO2 to replicate CO2 streams from sources in a CCS pipeline network. Due to the moisture content being only 50 ppmV no visible acid condensation took place. To simulate stress conditions at the inside pipeline surface due to fluid pressure (10 MPa) specimens were preloaded using a load frame. Experiments conducted at 278 K and at 313 K revealed the highest corrosion rate at lower temperature. Corrosive effect of impurities was strongest applying mixed atmosphere, containing oxidizing and reductive components, closely followed by CO2 streams with pure oxidizing character. By far, the lowest corrosion rate (10x lower) resulted from reductive atmosphere. In general, at constant temperature and pressure the CO2 stream composition strongly influences the morphology, thickness and composition of the corrosion products. Applying oxidizing or mixed impurities, iron hydroxides or oxides (e.g. goethite, hematite) occur as dominating corrosion products, capable to incorporate different amounts of sulfur. In contrast, using reductive atmosphere very thin corrosion layers with low crystallinity were developed, and phase identification by XRD was unfeasible. SEM/EDX analysis revealed the formation of Fe-O compounds, most likely attributed to the oxygen partial pressure in the system induced by CO2 (≥0.985 volume fraction) and volatile H2O. In addition to the surface covering corrosion layer, secondary phases had grown locally distributed on top of the layer. These compounds are characteristic for the applied atmosphere and vary in number, shape and chemical composition. T2 - 14th Greenhouse Gas Control Technologies Conference (GHGT-14) CY - Melbourne, Australia DA - 21.10.2018 KW - CCS KW - CO2 Corrosion KW - Pipelines PY - 2019 UR - https://ssrn.com/abstract=3365756 VL - 2019 SP - 1 EP - 15 AN - OPUS4-49711 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sommer, Konstantin A1 - Agudo Jácome, Leonardo A1 - Hesse, René A1 - Bettge, Dirk T1 - Revealing the nature of melt pool boundaries in additively manufactured stainless steel by nano-sized modulation N2 - In the current study, the 3D nature of the melt pool boundaries (MPBs) in a 316 L austenitic steel additively manufactured by laser-based powder bed fusion (L-PBF) is investigated. The change of the cell growth direction and its relationship to the MPBs is investigated by transmission electron microscopy. A hitherto unreported modulated substructure with a periodicity of 21 nm is further discovered within the cell cores of the cellular substructure, which results from a partial transformation of the austenite, which is induced by a Ga+ focused ion beam. While the cell cores show the modulated substructure, cell boundaries do not. The diffraction pattern of the modulated substructure is exploited to show a thickness ≥200 nm for the MPB. At MPBs, the cell walls are suppressed, leading to continuously connecting cell cores across the MPB. This continuous MPB is described either as overlapping regions of cells of different growing directions when a new melt pool solidifies or as a narrow planar growth preceding the new melt pool. KW - Additive manufacturing KW - Austenitic steel 316L KW - Melt pool boundary KW - Microstructural characterization KW - Transmission electron microscopy PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547295 DO - https://doi.org/10.1002/adem.202101699 SN - 1527-2648 VL - 24 IS - 6 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-54729 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kratzig, A. A1 - Le, Quynh Hoa A1 - Bettge, Dirk A1 - Menneken, M. A1 - Bäßler, Ralph T1 - Early Stage of Corrosion Formation on Pipeline Steel X70 Under Oxyfuel Atmosphere at Low Temperature N2 - The early stage of corrosion formation on X70 pipeline steel under oxyfuel atmosphere was investigated by applying a simulated gas mixture (CO2 containing 6700 ppmv O2, 100 ppmv NO2, 70 ppmv SO2 and 50 ppmv H2O) for 15 h at 278 K and ambient pressure. Short-term tests (6 h) revealed that the corrosion starts as local spots related to grinding marks progressing by time and moisture until a closed layer was formed. Acid droplets (pH 1.5), generated in the gas atmosphere, containing a mixture of H2SO4 and HNO3, were identified as corrosion starters. After 15 h of exposure, corrosion products were mainly X-ray amorphous and only partially crystalline. In-situ energy-dispersive X-ray diffraction (EDXRD) results showed that the crystalline fractions consist primarily of water-bearing iron sulfates. Applying Raman spectroscopy, water-bearing iron nitrates were detected as subordinated phases. Supplementary long-term tests exhibited a significant increase in the crystalline fraction and formation of additional water-bearing iron sulfates. All phases of the corrosion layer were intergrown in a nanocrystalline network. In addition, numerous globular structures have been detected above the corrosion layer, which were identified as hydrated iron sulphate and hematite. As a type of corrosion, shallow pit formation was identified, and the corrosion rate was about 0.1 mma−1. In addition to in-situ EDXRD, SEM/EDS, TEM, Raman spectroscopy and interferometry were used to chemically and microstructurally analyze the corrosion products. KW - Corrosion KW - CCUS KW - In-situ ED-XRD KW - CO2 pipeline transport KW - Oxyfuel KW - Carbon steel KW - Impurities PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-506303 DO - https://doi.org/10.3390/pr8040421 SN - 2227-9717 VL - 8 IS - 4 SP - 421-1 EP - 421-19 PB - MDPI CY - Basel AN - OPUS4-50630 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 -