TY - JOUR A1 - Nasrabadi, Hossein Beygi A1 - Hanke, T. A1 - Weber, M. A1 - Eisenbart, M. A1 - Bauer, F. A1 - Meissner, R. A1 - Dziwis, G. A1 - Tikana, L. A1 - Chen, Yue A1 - Skrotzki, Birgit T1 - Toward a digital materials mechanical testing lab N2 - To accelerate the growth of Industry 4.0 technologies, the digitalization of mechanical testing laboratories as one of the main data-driven units of materials processing industries is introduced in this paper. The digital lab infrastructure consists of highly detailed and standard-compliant materials testing knowledge graphs for a wide range of mechanical testing processes, as well as some tools that enable the efficient ontology development and conversion of heterogeneous materials’ mechanical testing data to the machine-readable data of uniform and standardized structures. As a basis for designing such a digital lab, the mechanical testing ontology (MTO) was developed based on the ISO 23718 and ISO/IEC 21838-2 standards for the semantic representation of the mechanical testing experiments, quantities, artifacts, and report data. The trial digitalization of materials mechanical testing lab was successfully performed by utilizing the developed tools and knowledge graph of processes for converting the various experimental test data of heterogeneous structures, languages, and formats to standardized Resource Description Framework (RDF) data formats. The concepts of data storage and data sharing in data spaces were also introduced and SPARQL queries were utilized to evaluate how the introduced approach can result in the data retrieval and response to the competency questions. The proposed digital materials mechanical testing lab approach allows the industries to access lots of trustworthy and traceable mechanical testing data of other academic and industrial organizations, and subsequently organize various data-driven research for their faster and cheaper product development leading to a higher performance of products in engineering and ecological aspects. KW - General Engineering KW - General Computer Science PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-582698 SN - 0166-3615 VL - 153 SP - 1 EP - 15 PB - Elsevier B.V. AN - OPUS4-58269 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chen, Yue A1 - Schilling, Markus A1 - von Hartrott, P. A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit A1 - Olbricht, Jürgen T1 - Ontopanel: A Tool for Domain Experts Facilitating Visual Ontology Development and Mapping for FAIR Data Sharing in Materials Testing N2 - In recent years, the design and development of materials are strongly interconnected with the development of digital technologies. In this respect, efficient data management is the building block of material digitization and, in the field of materials science and engineering (MSE), effective solutions for data standardization and sharing of different digital resources are needed. Therefore, ontologies are applied that represent a map of MSE concepts and relationships between them. Among different ontology development approaches, graphical editing based on standard conceptual modeling languages is increasingly used due to its intuitiveness and simplicity. This approach is also adopted by the Materials-open-Laboratory project (Mat-o-Lab), which aims to develop domain ontologies and method graphs in accordance with testing standards in the field of MSE. To suit the actual demands of domain experts in the project, Ontopanel was created as a plugin for the popular open-source graphical editor diagrams.net to enable graphical ontology editing. It includes a set of pipeline tools to foster ontology development in diagrams.net, comprising imports and reusage of ontologies, converting diagrams to Web Ontology Language (OWL), verifying diagrams using OWL rules, and mapping data. It reduces learning costs by eliminating the need for domain experts to switch between various tools. Brinell hardness testing is chosen in this study as a use case to demonstrate the utilization of Ontopanel. KW - Materials Testing KW - Ontology KW - Visual ontology development KW - Data mapping KW - FAIR PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-560339 SP - 1 EP - 12 PB - Springer AN - OPUS4-56033 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Chen, Yue A1 - Schilling, Markus A1 - von Hartrott, P. A1 - Huschka, M. A1 - Olbricht, Jürgen A1 - Pirskawetz, Stephan A1 - Skrotzki, Birgit A1 - Hanke, T. A1 - Todor, A. T1 - Ontopanel: a diagrams.net plugin for graphical semantic modelling N2 - Ontologies that represent a map of the concepts and relationships between them, are becoming an effective solution for data standardization and integration of different resources in the field of materials science, as efficient data storage and management is the building block of material digitization. However, building a domain ontology is not a simple task. It requires not only a collaborative effort between ontologists and domain experts, but also the modeling approaches and tools play a key role in the process. Among all approaches, graphical representation of domain ontologies based on standard conceptual modeling languages is widely used because of its intuitiveness and simplicity. Various tools have been developed to realize this approach in an intuitive way, such as Protégé plugins and web visualization tools. The Materials-open-Lab (MatOLab) project, which aims to develop ontologies and workflows in accordance with testing standards for the materials science and engineering domains, adopted a UML (Unified Modeling Language) approach based on the diagrams.net. It is a powerful, popular, open-source graphical editor. In practical case studies, however, many users’ needs could not be met, such as reusing ontology, conversion, and data mapping. Users must switch between different tools to achieve a certain step, and thereby invariably increase learning cost. The lack of validation also leads to incorrect diagrams and results for users who are not familiar with the ontology rules. To address these issues, we designed Ontopanel, a diagrams.net-based plugin that includes a set of pipeline tools for semantic modeling: importing and displaying protégé-like ontologies, converting diagrams to OWL, validating diagrams by OWL rules, and mapping data. It uses diagrams.net as the front-end for method modeling and Django as the back-end for data processing. As a web-based tool, it is very easy to expand its functionality to meet changing practical needs. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Ontology KW - Tools KW - Material digital KW - Mat-o-lab KW - Graphic design KW - Ontology development KW - Data mapping KW - FAIR KW - Materials testing PY - 2022 AN - OPUS4-55884 LA - eng 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 -