TY - CONF A1 - Fotheringham, U. T1 - Digitalization of Glass Development N2 - Im Vortrag werden erste Ergebnisse aus dem vom BMFTR im Rahmen der MaterialDigital Initiative geförderten Projekt „GlasAgent“ vorgestellt, welches die Glasentwicklung mittels KI vorantreiben soll. In diesem Projekt werden mehrere Entwicklungszyklen inklusive des Recyclingprozesses durchlaufen und die Ergebnisse genutzt, um Datenbanken und Modelle zu verbessern. Mit diesen verknüpft und basierend auf der semantischen GlasDigital-Ontologie soll zukünftig ein Chatbot die Glasentwicklung schneller, präziser und nachhaltiger gestalten. T2 - PMD Vollversammlung CY - Berlin, Germany DA - 26.11.2025 KW - Glass KW - Workflow KW - Automation KW - MAP KW - Ontology KW - Simulation KW - Database PY - 2025 AN - OPUS4-65039 LA - deu 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 - JOUR A1 - Chen, Ya‐Fan A1 - Arendt, Felix A1 - Bornhöft, Hansjörg A1 - de Camargo, Andréa S. S. A1 - Deubener, Joachim A1 - Diegeler, Andreas A1 - Gogula, Shravya A1 - Contreras Jaimes, Altair T. A1 - Kempf, Sebastian A1 - Kilo, Martin A1 - Limbach, René A1 - Müller, Ralf A1 - Niebergall, Rick A1 - Pan, Zhiwen A1 - Puppe, Frank A1 - Reinsch, Stefan A1 - Schottner, Gerhard A1 - Stier, Simon A1 - Waurischk, Tina A1 - Wondraczek, Lothar A1 - Sierka, Marek T1 - Ontology‐Based Digital Infrastructure for Data‐Driven Glass Development N2 - The development of new glasses is often hampered by inefficient trial‐and‐error approaches. The traditional glass manufacturing process is not only time‐consuming, but also difficult to reproduce with inevitable variations in process parameters. These challenges are addressed by implementing an ontology‐based digital infrastructure coupled with a robotic melting system. This system facilitates high‐throughput glass synthesis and ensures the collection of consistent process data. In addition, the digital infrastructure includes machine learning models for predicting glass properties and a tool for extracting patent information. Current glass databases have significant gaps in the relationships between compositions, process parameters, and properties due to inconsistent studies and nonconforming units. In addition, process parameters are often omitted, and even original literature references provide limited information. By continuously expanding the database with consistent, high‐quality data, it is aimed to fill these gaps and accelerate the glass development process. KW - Digitalisation KW - Data-driven glass development KW - Ontology PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-625844 DO - https://doi.org/10.1002/adem.202401560 SN - 1527-2648 SP - 1 EP - 12 PB - Wiley VHC-Verlag AN - OPUS4-62584 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Glauer, M. A1 - Schilling, Markus A1 - Stappel, M. T1 - A practical ontology development guide N2 - Knowledge representation is becoming increasingly important in view of the large amounts of data that are handled in the modern scientific landscape. Many of the domains that have most readily realised this problem and worked on potential solutions have been the domain of biochemistry. And although the developments sought here were not accompanied by philosophers, this process organically led to the development of formal and structured representations of certain domains. While the resulting structures were not the first formal ontologies, they are ones that are still in wide use to this day. These approaches have led to major advances in the organisation, structuring and communication of scientific results. Since then, a variety of other domains have tried to adapt a similar process and develop their own ontologies. However, the development of ontologies from the domain of biochemistry was the result of a years-long process that also involved a large number of errors and course corrections. One of the greatest challenges is also one of the greatest strengths of ontologies: Interoperability with other ontologies. To ensure this interoperability, ontologies must follow certain principles. In the field of biochemistry, the OBO Foundry has established itself, which offers functionalities for a rich network of ontologies from the domain, but at the same time also defines rules. The purpose of this document is to define a similar set of rules for open ontology development, but which addresses a broader domain and at the same time lowers the barrier of entry for new ontology developers. To this end, we will outline a workflow that can be used to build new ontologies more efficiently. This workflow is based not only on our own years of experience in ontology development, but also on the rules of external experts such as the OBO Foundry. KW - Ontology KW - Data integration KW - Ontologie KW - Data interoperability PY - 2024 UR - https://scientific-ontology-network.github.io/ UR - https://github.com/scientific-ontology-network/ontology-development-guide/releases/download/v0.1.0/ontology-guide.pdf SP - 1 EP - 27 PB - GitHub CY - San Francisco ET - Version 0.1 AN - OPUS4-61140 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 - Skrotzki, Birgit T1 - Digital representation of materials testing data for semantic web analytics: Tensile stress relaxation testing use case N2 - This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web. KW - Digitalization KW - Tensile stress relaxation KW - Ontology KW - Mechanical testing KW - Semantic web PY - 2024 DO - https://doi.org/10.4028/p-xSmHN2 VL - 987 SP - 47 EP - 52 PB - Trans Tech Publications Ltd CY - Switzerland AN - OPUS4-61152 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Birkholz, Henk A1 - Bayerlein, Bernd T1 - Evolution of the PMD Core Ontology (PMDco) Towards ISO/IEC 21838-2:2021 Basic Formal Ontology (BFO) N2 - Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific. In this presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD Core Ontology (PMDco)–via MSE community-based curation procedures is shown. The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications. T2 - MSE Congress 2024 - Materials Science and Engineering CY - Darmstadt, Germany DA - 24.09.2024 KW - Ontology KW - Semantic Interoperability KW - Knowledge representation KW - FAIR Data Management KW - PMD Core Ontology PY - 2024 AN - OPUS4-61141 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mieller, Björn T1 - Ontologies and data pipelines - a field report from the development of multilayer ferrite inductors N2 - Digitalization is a current and prominent cross-cutting topic in ceramics and materials science in general. Many research initiatives and levels of significance are associated with this term. The Initiative Platform MaterialDigital (PMD), for example, aims to create a material data space filled with semantically linked data. The concept envisages that semantic relationships between the data are described as ontologies and that processing of data takes place via automated data pipelines. Various research projects from all areas of materials science are working on the implementation of this concept based on specific use cases. In the project presented here, the use case is the development of multilayer ferrite inductors as passive microelectronic components. The inductors are fabricated by ceramic multilayer technology and co-firing of metallized tapes of NiCuZn ferrite and a dielectric base material. Investigations focus on the effects of fabrication technology on the permeability of the ferrite. A data pipeline is introduced that automatically processes the unstructured experimental data into structured, machine-readable and semantically linked data. The concrete implementation of the data pipeline and a domain ontology is presented using examples. Challenges and advantages are discussed. T2 - CERAMICS 2024 / 99th DKG Annual Meeting CY - Höhr-Grenzhausen, Germany DA - 09.09.2024 KW - Ontology KW - Ceramic multilayer KW - MaterialDigital PY - 2024 AN - OPUS4-61037 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Crafting High-Quality, Reliable, and FAIR Data: From Metadata, Schema and Ontologies to Data Management and Knowledge Transfer N2 - Following the new paradigm of materials development, design and optimization, digitalization is the main goal in materials sciences (MS) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR (findability, accessibility, interoperability, reusability) principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web Technologies (SWT) are used since they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use. In this respect, the motivation for digital transformation in materials sciences stemming from the need to handle the ever-increasing volume and complexity of data will be elaborated on. By embracing digital tools and methodologies, researchers can enhance the efficiency, accuracy, and reproducibility of their work. The benefits of digital transformation in materials sciences are manifold, including improved data management, enhanced collaboration, and accelerated innovation. Being a core component of this transformation, ensuring data reliability and reproducibility is critical for the advancement of the field, enabling researchers to build on each other's work with confidence. Implementing FAIR data principles facilitates this by making data more accessible and usable across different platforms and studies. Furthermore, Semantic Web technologies (SWT) and ontologies play a crucial role in achieving these goals. Ontologies, typically consisting of the T-Box (terminological component) and A-Box (assertional component), provide a structured framework for representing knowledge. This presentation will outline the path of ontology creation and the formal transformation procedure, highlighting the various ontology levels that organize data into meaningful hierarchies. Real-world use cases presented, such as the Tensile Test Ontology (TTO) and the Orowan Demonstrator, illustrate the practical applications of these technologies. These examples will demonstrate how ontologies can be leveraged to standardize data and facilitate interoperability between different systems and research groups. Finally, in this presentation, Ontopanel is introduced, a tool designed to aid in the creation and management of ontologies. Ontopanel simplifies the process of developing and maintaining ontologies, making it accessible to researchers and practitioners in the field. By integrating these technologies and principles, the materials science community can move towards a more digital, interconnected, and efficient future making the knowledge and education on these topics very valuable. T2 - MaRDA MaRCN FAIR Train Workshop CY - Washington, DC, USA DA - 29.07.2024 KW - FAIR KW - Metadata KW - Digitalization KW - Data Interoperability KW - Ontology KW - Education KW - Workshop PY - 2024 AN - OPUS4-60720 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mieller, Björn A1 - Hassine, Sahar Ben A1 - Töpfer, Jörg A1 - Priese, Christoph A1 - Bochmann, Arne A1 - Capraro, Beate A1 - Stark, Sebastian A1 - Partsch, Uwe A1 - Fresemann, Carina T1 - Ontology‐based Data Acquisition, Refinement, and Utilization in the Development of a Multilayer Ferrite Inductor N2 - A key aspect in the development of multilayer inductors is the magnetic permeability of the ferrite layers. Here, the effects of different processing steps on the permeability of a NiCuZn ferrite is investigated. Dry pressed, tape cast, and co‐fired multilayer samples are analyzed. An automated data pipeline is applied to structure the acquired experimental data according to a domain ontology based on PMDco (Platform MaterialDigital core ontology). Example queries to the ontology show how the determined process‐property correlations are accessible to non‐experts and thus how suitable data for component design can be identified. It is demonstrated how the inductance of co‐fired multilayer inductors is reliably predicted by simulations if the appropriate input data corresponding to the manufacturing process is used.This article is protected by copyright. All rights reserved. KW - Ontology KW - Ceramic multilayer KW - Data pipeline PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-605483 DO - https://doi.org/10.1002/adem.202401042 SN - 1527-2648 SP - 1 EP - 11 PB - Wiley-VCH CY - Weinheim AN - OPUS4-60548 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -