TY - CONF A1 - Bayerlein, Bernd T1 - Towards Interoperability: Digital Representation of a Material Specific Characterization Method N2 - Certain metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which represent obstacles to dislocation movement. Precise tuning of the material structure is critical for optimal mechanical behavior in the application. Therefore, analysis of the microstructure and especially the precipitates is essential to determine the ideal parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in a first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and to quantify their relevant dimensions. The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, an automatable, digital approach is demonstrated. Based on DF-TEM images of different precipitation states of a wrought aluminum alloy, a modularizable digital workflow for quantitative precipitation analysis is presented. The integration of this workflow into a data pipeline concept will also be discussed. Thus, by using ontologies, the raw image data, their respective contextual information, and the resulting output data from the quantitative precipitation analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure the reproducibility of the data. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability. The presented work is part of two digitization initiatives, the Platform MaterialDigital (PMD, materialdigital.de) and Materials-open-Laboratory (Mat-o-Lab). T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Interoperability KW - Ontology KW - Precipitation Analysis PY - 2022 AN - OPUS4-55892 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Digital Transformation in Materials Science:Insights from Platform MaterialDigital N2 - In the digital transformation of materials science and engineering, existing processes are being closely examined to optimize and extend them using technologies such as the Semantic Web, while adhering to the FAIR principles. The challenge is to identify and adapt these technologies to significantly improve daily operations, covering the entire cycle of data generation, collection, storage to its processing and use. This presentation presents different approaches developed within the Platform MaterialDigital (PMD) and related initiatives, each addressing specific challenges of digitalization and demonstrating their benefits. These will be illustrated by a case study on age-hardenable aluminium alloys in different aging states, based on a comprehensive dataset of microstructural and mechanical properties. In this context, ontologies are essential for organizing data into interoperable semantic structures, thereby enabling the representation of concepts and their relationships. The PMD Core Ontology (PMDco) serves as a higher semantic layer that connects domain-specific ontologies [1]. The PMDco provides MSE-specific bridging concepts that enhance data mapping and integration capabilities. This approach facilitates the linking of results from natural language processing of a corpus of microscopy abstracts, which supported the development of a microscopy ontology. In addition, the presentation highlights how modular, digital workflows can improve the reproducibility of transmission electron microscopy image processing and analysis [2]. It also shows how freely available mechanical and microstructural datasets are semantically integrated and made searchable through PMDco, facilitating correlation analysis [3]. Looking ahead, the integration of these components has the potential to advance and sustain MSE practices. T2 - MSE Congress 2024 - Materials Science and Engineering CY - Darmstadt, Germany DA - 24.09.2024 KW - Semantic Interoperability KW - FAIR data management KW - Digtial Representation PY - 2024 AN - OPUS4-61137 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - von Hartrott, Philipp A1 - Waitelonis, Jörg A1 - Birkholz, Henk A1 - Portella, Pedro Dolabella A1 - Skrotzki, Birgit T1 - FAIR and Structured Data: A Domain Ontology Aligned with Standard‐Compliant Tensile Testing N2 - The digitalization of materials science and engineering (MSE) is currently leading to remarkable advancements in materials research, design, and optimization, fueled by computer‐driven simulations, artificial intelligence, and machine learning. While these developments promise to accelerate materials innovation, challenges in quality assurance, data interoperability, and data management have to be addressed. In response, the adoption of semantic web technologies has emerged as a powerful solution in MSE. Ontologies provide structured and machine‐actionable knowledge representations that enable data integration, harmonization, and improved research collaboration. This study focuses on the tensile test ontology (TTO), which semantically represents the mechanical tensile test method and is developed within the project Plattform MaterialDigital (PMD) in connection with the PMD Core Ontology. Based on ISO 6892‐1, the test standard‐compliant TTO offers a structured vocabulary for tensile test data, ensuring data interoperability, transparency, and reproducibility. By categorizing measurement data and metadata, it facilitates comprehensive data analysis, interpretation, and systematic search in databases. The path from developing an ontology in accordance with an associated test standard, converting selected tensile test data into the interoperable resource description framework format, up to connecting the ontology and data is presented. Such a semantic connection using a data mapping procedure leads to an enhanced ability of querying. The TTO provides a valuable resource for materials researchers and engineers, promoting data and metadata standardization and sharing. Its usage ensures the generation of finable, accessible, interoperable, and reusable data while maintaining both human and machine actionability. KW - Data Interoperability KW - Domain Ontology Development KW - FAIR Data Management KW - Knowledge Representation KW - Tensile Test Ontology KW - Semantic Web Technologies PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-598142 DO - https://doi.org/10.1002/adem.202400138 SN - 1438-1656 SP - 1 EP - 19 PB - Wiley VHC-Verlag AN - OPUS4-59814 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Probenpräparation für AFM-basierte Untersuchungsverfahren N2 - Adapted and advanced sample preparation of semiconductor layer systems with the focused ion beam for AFM-based test methods N2 - Der vorliegende Vortrag gibt einen Überblick über Probenpräparationen mit der Focused Ion Beam (FIB) für AFM-basierte Untersuchungsverfahren. Anhand zweier Beispiele wird gezeigt, wie ionenstrahlpolierte Lamellen aus Halbleiter-Schichtsystemen elektrisch leitfähig auf Substrate platziert werden, so dass in-situ und in-operando Messungen mit Scanning Microwave Microscope (SMM) bzw. Spectroscopic infrared scanning near-field optical microscope (IR-SNOM) durchgeführt werden können. T2 - 15. Berlin-Brandenburger Präparatorentreffen CY - Potsdam/Golm, Germany DA - 11.04.2019 KW - Focused Ion Beam KW - AFM based test methods KW - Sample preparation KW - Semiconductor materials KW - Layer system PY - 2019 AN - OPUS4-47784 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zia, Ghezal Ahmad Jan A1 - Hanke, Thomas A1 - Skrotzki, Birgit A1 - Völker, Christoph A1 - Bayerlein, Bernd T1 - Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing N2 - AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. KW - Industrial and Manufacturing Engineering KW - General Materials Science KW - Automated image analysis KW - FAIR research data management KW - Reproducibility KW - microstructural changes PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593905 DO - https://doi.org/10.1007/s40192-023-00331-5 SN - 2193-9772 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Heidelberg AN - OPUS4-59390 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Digitalisierung der Materialien in PMD & Mat-o-Lab - Eine normkonforme Anwendungsontologie des Zugversuchs N2 - Zur Bewältigung der Herausforderung bei der Digitalisierung von Materialien und Prozessen ist eine mit allen Stakeholdern konsistente Kontextualisierung von Materialdaten anzustreben, d.h. alle erforderlichen Informationen über den Zustand des Materials einschließlich produktions- und anwendungsbezogener Änderungen müssen über eine einheitliche, maschinenlesbare Beschreibung verfügbar gemacht werden. Dazu werden Wissensrepräsentationen und Konzeptualisierungen ermöglichende Ontologien verwendet. Erste Bemühungen in den beiden Projekten Plattform Material Digital und Materials-open-Laboratory führten zur Erstellung von Anwendungsontologien, die Prozesse und Testmethoden explizit beschreiben. Dabei wurde u.a. der Zugversuch an Metallen bei Raumtemperatur nach DIN EN ISO 6892-1 ontologisch beschrieben. Diese als Beispiel dienende Ontologieentwicklung wird in dieser Präsentation vorgestellt. T2 - Werkstoffprüfung 2021 CY - Online meeting DA - 02.12.2021 KW - Ontologie KW - Semantisches Web KW - Wissensrepräsentation KW - Digitalisierung KW - Zugversuch PY - 2021 AN - OPUS4-53929 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus A1 - Marschall, Niklas A1 - Bayerlein, Bernd A1 - Chen, Yue A1 - Olbricht, Jürgen A1 - Skrotzki, Birgit A1 - von Hartrott, P. A1 - Portella, Pedro Dolabella A1 - Waitelonis, J. A1 - Birkholz, H. A1 - Grundmann, J. ED - Zimmermann, M. T1 - Zugversuchsdaten FAIR integriert: Von einer normenkonformen Ontologie bis zu interoperablen Daten im Triple Store N2 - Das hochaktuelle Thema der Integration und Wiederverwendung von Wissen und Daten aus Herstellung, Bearbeitung und Charakterisierung von Materialien ('Digitalisierung von Materialien') wird in den Projekten Innovationsplattform MaterialDigital (PMD, materialdigital.de) und Materials-open-Lab (Mat-o-Lab, matolab.org) adressiert. In diesem Beitrag werden die Weiterentwicklungen in diesen Projekten hinsichtlich der Speicherung von Zugversuchsdaten gemäß einer normenkonformen (DIN EN ISO 6892-1:2019-11) ontologischen Repräsentation vorgestellt. Das umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verknüpfung von Ontologie und Daten. Letztendlich können die entsprechenden Daten in einem Triple Store abgelegt und abgefragt werden. T2 - Werkstoffprüfung 2022 CY - Dresden, Germany DA - 27.10.2022 KW - Ontology KW - Semantic Web KW - Digitalization KW - Knowledge Representation KW - Tensile Test PY - 2022 UR - https://dgm.de/fileadmin/DGM/Veranstaltungen/2022/Werkstoffpruefung/Tagungsband/WP2022-Tagungsband-online.pdf SN - 978-3-88355-430-3 SP - 105 EP - 110 PB - DGM - Deutsche Gesellschaft für Materialkunde e.V CY - Sankt Augustin AN - OPUS4-56836 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Adopting FAIR data practices in materials science: Semantic representation of a quantitative precipitation analysis N2 - Many metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminium alloys, the strengthening mechanism is based on the controlled formation of nanometre-sized precipitates, which represent obstacles to dislocation movement and consequently increase the strength. Precise tuning of the material microstructure is thus crucial for optimal mechanical behaviour under service condition of a component. Therefore, analysis of the microstructure, especially the precipitates, is essential to determine the optimum parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in the first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and thereafter quantify their relevant dimensions. Often, these evaluations are still performed by manual image analysis, which is very time-consuming and to some extent also poses reproducibility problems. Our work aims at a semantic representation of an automatable digital approach for this material specific characterization method under adaption of FAIR data practices. Based on DF-TEM images of different precipitation states of a wrought aluminium alloy, the modularizable, digital workflow of quantitative analysis of precipitate dimensions is described. The integration of this workflow into a data pipeline concept will also be discussed. Using ontologies, the raw image data, their respective contextual information, and the resulting output data of the quantitative image analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure data reproducibility. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability. T2 - FEMS EUROMAT 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Semantic Representation KW - FAIR data management KW - Quantitative Precipitation Analysis KW - Knowledge graph and ontologies PY - 2023 AN - OPUS4-58199 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -