TY - CONF A1 - Beygi Nasrabadi, Hossein T1 - Domain-level ontology formulation based on the Platform Material Digital (PMD) ontology: case study Brinell hardness N2 - A large amount of publicly available data is reproduced every day in the field of materials science, while these kind of material data can have different formats and types like paper-type publications, standards, datasheets or isolated datasets in repositories. However, gathering a specific library from such extensive and diverse material data is always challenging for the materials scientists and engineers, since the time-related limitations are not allowed to fully access the large publicly available databases; search across these disparate databases, manage the large volumes of heterogeneous datasets, and integrate data from multiple sources. To address these challenges and make data findable, accessible, interoperable, and reusable (FAIR), an efficient data management system is necessary to build comprehensive, documented, and connected data spaces in the future. A formal standardized knowledge representation through an ontology can address such problems and make data more available and interoperable between related domains. Ontology can also rich machine processable semantic descriptions that increases the performance of scientific searches. In this regard, the Platform MaterialDigital (PMD) is currently working on developing a high-level ontology for the materials and material related processes. For example, in one of the PMD projects of “KupferDigital”, we will try to develop a data ecosystem for digital materials research based on ontology-based digital representations of copper and copper alloys. As a case study, this paper describes the methodology for ontology development of Brinell hardness, based on PMD core ontology. The methodology we describe includes the following steps; gathering the required domain terminology from different resources like standards (DIN EN ISO 6506-1) and test reports, representing the performance of a standard-conformant hardness test and the treatment of the recorded values up to a “reportable” hardness value for a material, designing the process chain according to the semantic technologies, and developing a domain-level ontology of Brinell hardness based on PMD ontology semantic formalization. Apart the mentioned methodology, some interesting tools and methods were introduced and ontology design challenges and possible solutions for modelling materials and processes were discussed. Furthermore, a dataset from the Brinell hardness measurement of cast copper samples is prepared for testing the query process. T2 - MSE 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Copper KW - Materials Science KW - Ontology KW - Platform MaterialDigital (PMD) KW - Brinell hardness PY - 2022 AN - OPUS4-56092 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit T1 - Ontology-Oriented Modeling of the Vickers Hardness Knowledge Graph N2 - This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries. KW - Data Mapping KW - FAIR Data KW - Ontology KW - Knowledge Graph KW - Vickers Hardness PY - 2024 DO - https://doi.org/10.4028/p-k8Gj2L VL - 149 SP - 33 EP - 38 PB - Trans Tech Publications Ltd CY - Switzerland AN - OPUS4-59981 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Waitelonis, Jörg A1 - Birkholz, Henk A1 - Jung, Matthias A1 - Schilling, Markus A1 - v. Hartrott, Philipp A1 - Bruns, Marian A1 - Schaarschmidt, Jörg A1 - Beilke, Kristian A1 - Mutz, Marcel A1 - Nebel, Vincent A1 - Königer, Veit A1 - Beran, Lisa A1 - Kraus, Tobias A1 - Vyas, Akhilesh A1 - Vogt, Lars A1 - Blum, Moritz A1 - Ell, Basil A1 - Chen, Ya‐Fan A1 - Waurischk, Tina A1 - Thomas, Akhil A1 - Durmaz, Ali Riza A1 - Ben Hassine, Sahar A1 - Fresemann, Carina A1 - Dziwis, Gordian A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Telong, Melissa A1 - Pirskawetz, Stephan A1 - Kamal, Mohamed A1 - Bjarsch, Thomas A1 - Pähler, Ursula A1 - Hofmann, Peter A1 - Leemhuis, Mena A1 - Özçep, Özgür L. A1 - Meyer, Lars‐Peter A1 - Skrotzki, Birgit A1 - Neugebauer, Jörg A1 - Wenzel, Wolfgang A1 - Sack, Harald A1 - Eberl, Chris A1 - Portella, Pedro Dolabella A1 - Hickel, Tilmann A1 - Mädler, Lutz A1 - Gumbsch, Peter T1 - Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science N2 - This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value. KW - Semantic Interoperability KW - Data Spaces KW - Integration Workflows KW - MaterialDigital PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622952 DO - https://doi.org/10.1002/adem.202401092 SN - 1527-2648 SP - 1 EP - 25 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62295 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beygi Nasrabadi, Hossein A1 - Bauer, Felix A1 - Uhlemann, Patrick A1 - Thärig, Steffen A1 - Rehmer, Birgit A1 - Skrotzki, Birgit T1 - Mechanical testing dataset of cast copper alloys for the purpose of digitalization N2 - This data article presents a set of primary, analyzed, and digitalized mechanical testing datasets for nine copper alloys. The mechanical testing methods including the Brinell and Vickers hardness, tensile, stress relaxation, and low-cycle fatigue (LCF) testing were performed according to the DIN/ISO standards. The obtained primary testing data (84 files) mainly contain the raw measured data along with the testing metadata of the processes, materials, and testing machines. Five secondary datasets were also provided for each testing method by collecting the main meta- and measurement data from the primary data and the outputs of data analyses. These datasets give materials scientists beneficial data for comparative material selection analyses by clarifying the wide range of mechanical properties of copper alloys, including Brinell and Vickers hardness, yield and tensile strengths, elongation, reduction of area, relaxed and residual stresses, and LCF fatigue life. Furthermore, both the primary and secondary datasets were digitalized by the approach introduced in the research article entitled “Toward a digital materials mechanical testing lab” [1]. The resulting open-linked data are the machine-processable semantic descriptions of data and their generation processes and can be easily queried by semantic searches to enable advanced data-driven materials research. KW - FAIR principles KW - Hardness KW - Low-Cycle Fatigue (LCF) KW - Tensile testing KW - Stress relaxation PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605005 DO - https://doi.org/10.1016/j.dib.2024.110687 SN - 2352-3409 SP - 1 EP - 15 PB - Elsevier BV AN - OPUS4-60500 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eisenbart, Miriam A1 - Hanke, Thomas A1 - Bauer, Felix A1 - Beygi Nasrabadi, Hossein A1 - Junghanns, Kurt A1 - Dziwis, Gordian A1 - Tikana, Ladji A1 - Parvez, Ashak Mahmud A1 - van den Boogaart, Karl Gerald A1 - Sajjad, Mohsin A1 - Friedmann, Valerie A1 - Preußner, Johannes A1 - Ramakrishnan, Anantha Narayanan A1 - Klengel, Sandy A1 - Meyer, Lars‐Peter A1 - Martin, Michael A1 - Klotz, Ulrich Ernst A1 - Skrotzki, Birgit A1 - Weber, Matthias T1 - KupferDigital: Ontology‐Based Digital Representation for the Copper Life Cycle N2 - The copper life cycle comprises numerous stages from the alloy production to the manufacturing and usage of engineered parts until recycling. At each step, valuable data are generated and stored; some are transferred to the subsequent stations. A thorough understanding of the materials’ behavior during manufacturing processes or throughout their product lifetime is highly dependent on a reliable data transfer. If, for example, a failure occurs during the service life, information about the manufacturing route can be of decisive importance for detecting the root cause of the failure. Additionally, the life cycle assessment hinges on the availability of data. Recording and storing interoperable structured data is, therefore, a thriving research field with huge implications for the economic strength of the manufacturing industry. In the KupferDigital project, it is demonstrated how an ontology‐based data space can be utilized not only as an innovative method for storing and providing interoperable life cycle data but also as a means to enable automated data analysis and evaluation, leading to new insights and the creation of new knowledge using semantic data and technologies. This work illustrates how data recorded at different research facilities can be integrated into one single data space, allowing queries across heterogeneous sources. KW - Copper Alloy KW - Ontology KW - Digitalization KW - Data Space KW - Semantic Representation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630213 SN - 1527-2648 DO - https://doi.org/10.1002/adem.202401735 VL - 27 IS - 8 SP - 1 EP - 29 PB - Wiley AN - OPUS4-63021 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit T1 - Ontology-oriented modeling of the Vickers hardness knowledge graph N2 - This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries. T2 - ICMMM 2023: 10th International Conference on Mechanics, Materials and Manufacturing CY - Washington, D.C., USA DA - 18.08.2023 KW - Ontology KW - Knowledge graph KW - Data mapping KW - Vickers hardness KW - FAIR data PY - 2023 AN - OPUS4-58100 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit T1 - Tensile test ontology (TTO) N2 - Tensile Test Ontology (TTO) has developed for representing the Tensile testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 6892-1 standard. Versions info: V2 developed using BFO+CCO top-level ontologies. V3 developed using PROVO+PMDco top-level ontologies. Repositories: GitLab: https://gitlab.com/kupferdigital/process-graphs/tensile-test GitHub: https://github.com/HosseinBeygiNasrabadi/Tensile-Test-Ontology-TTO- MatPortal: https://matportal.org/ontologies/TTO IndustryPortal: https://industryportal.enit.fr/ontologies/TTO KW - Ontology KW - Tensile testing PY - 2024 UR - https://gitlab.com/kupferdigital/process-graphs/tensile-test UR - https://github.com/HosseinBeygiNasrabadi/Tensile-Test-Ontology-TTO- UR - https://matportal.org/ontologies/TTO UR - https://industryportal.enit.fr/ontologies/TTO PB - GitLab CY - San Francisco, CA, USA AN - OPUS4-60545 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beygi Nasrabadi, Hossein A1 - Norouzi, Ebrahim A1 - Sack, Harald A1 - Skrotzki, Birgit T1 - Performance Evaluation of Upper‐Level Ontologies in Developing Materials Science Ontologies and Knowledge Graphs N2 - This study tackles a significant challenge in ontology development for materials science: selecting the most appropriate upper‐level ontologies for creating application‐level ontologies and knowledge graphs. Focusing on the use case of Brinell hardness testing, the research assesses the performance of various top‐level ontologies (TLOs)—basic formal ontology (BFO), elementary multiperspective material ontology (EMMO), and provenance ontology (PROVO)—in developing Brinell testing ontologies (BTOs). Consequently, three versions of BTOs are created using combinations of these TLOs along with their integrated mid‐ and domain‐level ontologies. The performance of these ontologies is evaluated based on ten parameters: semantic richness, domain coverage, extensibility, complexity, mapping efficiency, query efficiency, integration with other ontologies, adaptability to different data contexts, community acceptance, and documentation and maintainability. The results show that all candidate TLOs can effectively develop BTOs, each with its distinct advantages. BFO provides a well‐structured, understandable hierarchy, and excellent query efficiency, making it suitable for integration across various ontologies and applications. PROVO demonstrates balanced performance with strong integration capabilities. Meanwhile, EMMO offers high semantic richness and domain coverage, though its complex structure impacts query efficiency and integration with other ontologies. KW - Materials Science KW - Ontology KW - Knowledge Graph PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-612227 DO - https://doi.org/10.1002/adem.202401534 SN - 1527-2648 SP - 1 EP - 18 PB - Wiley AN - OPUS4-61222 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Beygi Nasrabadi, Hossein A1 - Hanke, Thomas A1 - Skrotzki, Birgit T1 - Semantic Representation of Low‐Cycle‐Fatigue Testing Data Using a Fatigue Test Ontology and ckan.kupferdigital Data Management System N2 - Addressing a strategy for publishing open and digital research data, this article presents the approach for streamlining and automating the process of storage and conversion of research data to those of semantically queryable data on the web. As the use case for demonstrating and evaluating the digitalization process, the primary datasets from low‐cycle‐fatigue testing of several copper alloys are prepared. The fatigue test ontology (FTO) and ckan.kupferdigital data management system are developed as two main prerequisites of the data digitalization process. FTO has been modeled according to the content of the fatigue testing standard and by reusing the basic formal ontology, industrial ontology foundry core ontology, and material science and engineering ontology. The ckan.kupferdigital data management system is also constructed in such a way that enables the users to prepare the protocols for mapping the datasets into the knowledge graph and automatically convert all the primary datasets to those machine‐readable data which are represented by the web ontology language. The retrievability of the converted digital data is also evaluated by querying the example competency questions, confirming that ckan.kupferdigital enables publishing open data that can be highly reused in the semantic web. KW - Accessible KW - CKAN KW - Interoperable KW - Digitalizations KW - Ontologies KW - Reusable data KW - Fatigue testing PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-604991 UR - https://onlinelibrary.wiley.com/doi/10.1002/adem.202400675 DO - https://doi.org/10.1002/adem.202400675 SN - 1527-2648 SP - 1 EP - 11 PB - Wiley AN - OPUS4-60499 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Beygi Nasrabadi, Hossein A1 - Skrotzki, Birgit T1 - Tensile stress relaxation test ontology (TSRTO) N2 - Tensile Stress Relaxation Test Ontology (TSRTO) has developed for representing the tensile stress relaxation testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 10319-1 standard. Versions info: V1 developed using BFO+CCO top-level ontologies. V3 developed using PROV+PMDco top-level ontologies. Repositories: GitLab: https://gitlab.com/kupferdigital/process-graphs/relaxation-test GitHub: https://github.com/HosseinBeygiNasrabadi/Tensile-Stress-Relaxation-Test-Ontology-TSRTO MatPortal: https://matportal.org/ontologies/TSRTO IndustryPortal: https://industryportal.enit.fr/ontologies/TSRTO KW - Ontology KW - Tensile stress relaxation testing KW - FAIR data PY - 2024 UR - https://gitlab.com/kupferdigital/process-graphs/relaxation-test UR - https://github.com/HosseinBeygiNasrabadi/Tensile-Stress-Relaxation-Test-Ontology-TSRTO UR - https://matportal.org/ontologies/TSRTO UR - https://industryportal.enit.fr/ontologies/TSRTO PB - GitLab CY - San Francisco, CA, USA AN - OPUS4-60546 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -