TY - GEN A1 - Schilling, Markus T1 - Tensile Test Ontology (TTO) N2 - This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature. The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated. The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse. KW - Ontology KW - Tensile Test KW - Digitalization KW - Plattform MaterialDigital KW - Structured Data PY - 2023 UR - https://github.com/MarkusSchilling/application-ontologies/blob/479311832819af695a2c64fa8eb772f2da398061/tensile_test_ontology_TTO/pmd_tto.ttl UR - https://github.com/materialdigital/core-ontology/blob/59f5727b0437ceea5e3d9fcb8fcd0ac211e92cc3/pmd_tto.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57935 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - Birkholz, H. A1 - Fliegener, S. A1 - Grundmann, J. A1 - Hanke, T. A1 - von Hartrott, P. A1 - Waitelonis, J. T1 - PMD Core Ontology (PMDco) N2 - The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. PMDco's class structure is both understandable and extensible, making it an efficient tool for organizing MSE knowledge. It serves as a semantic intermediate layer that unifies MSE knowledge representations, enabling data and metadata to be systematically integrated on key terms within the MSE domain. With PMDco, it is possible to seamlessly trace data generation. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the generation, derivation, and attribution of resources. By building on this foundation, PMDco facilitates the integration of data from various sources and the creation of complex workflows. In summary, PMDco is a valuable tool for researchers and practitioners in the MSE domains. It provides a common language for representing and sharing knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless traceability of data generation. Overall, PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain. PMDco at GitHub: https://github.com/materialdigital/core-ontology KW - Ontology KW - Semantic Web technologies KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2023 UR - https://github.com/materialdigital/core-ontology/blob/f2bd420348b276583fad6fa0fb4225f17b893c78/pmd_core.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-59352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - PMDco - Platform MaterialDigital Core Ontology: Achieving High-Quality & Reliable FAIR Data 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 poster 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 - DVM Arbeitskreis Betriebsfestigkeit - Potenziale der Betriebsfestigkeit in Zeiten des technologischen und gesellschaftlichen Wandels CY - Munich, Germany DA - 11.10.2023 KW - Digitalization KW - Semantic Web Technologies KW - FAIR KW - Data Interoperability KW - PMD Core Ontology PY - 2023 AN - OPUS4-58602 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eisenbart, Miriam T1 - Digitalization of a high-throughput alloy development process N2 - Digitalization is nowadays the central key in developing pharmaceutical products, but it also becomes increasingly important in computer aided material development. In this work it is presented how a collaborative ontology development for the alloy development process is approached and a workflow for chemical optimization of copper alloys is introduced. This is done by employing a combination of a high-throughput alloy development method [1] with the calculation of the corresponding phase compositions. The work presented here is part of the publicly funded project KupferDigital. The project is associated with the innovation platform MaterialDigital (PMD) and focusses on the digitalization of processes as well as knowledge representation along the life-cycle of copper and copper alloys. One part of this life cycle is the alloy development of copper alloys, in this case the alloy development employing a high throughput method based on diffusion couples. The alloy development process can be further broken down into several typical material processing steps such as casting, diffusion welding and annealing. As part of the copper life cycle, the constitution and the properties of an alloy play an important role for processes further down the road during product manufacturing and service life and are also crucial for the recycling properties of the alloy. It is therefore important to, on the one hand, communicate data concerning properties, process history and constitution to the following stations of the life cycle, on the other hand, it is also elementary for the material scientist to be aware of the recycling properties of the alloys constituents. This motivates sharing of data along the life cycle and the development of ontology based data spaces, where life-cycle information can be linked across all involved domains. As part of the alloy development process, it is presented how experimental data created at fem are shared using linked data and ontologies with an example based on the digital representation of the chemical composition of copper alloys further processed by CALculation of PHAse Diagrams (CALPHAD) at the Fraunhofer IWM. The calculation results are used to correlate measured hardness data with the equilibrium phases of the alloys. Selected Compositions are cast and heat treated and subjected to mechanical testing at BAM and the resulting mechanical property-data are again linked to the measured and simulated data from the alloy development process. T2 - MSE2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Digitalization KW - Alloy development PY - 2024 AN - OPUS4-61150 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: Insights from Platform MaterialDigital (PMD), Tensile Test Ontology (TTO), Electronic Lab Notebooks (ELN) N2 - The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations. T2 - NIST Seminar Series CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Semantic Data KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - NIST KW - Tensile Test Ontology KW - Elctronic Lab Notebook PY - 2024 AN - OPUS4-60392 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - de Camargo, Andrea Simone Stucchi T1 - Glass Digitalization: Contributions from BAM N2 - An overview of the Glass Digitalization efforts at BAM, within the framework of the Glass Digital consortium, was given. From the development of the robotic melting device to the ML capabilities, a description of the different stages of the developments and roles of project partner was presented. T2 - GlaCerHub Melting Day CY - Oponice, Slovakia DA - 12.06.2024 KW - Glass Digital KW - Robotic glass melting KW - Digitalization PY - 2024 AN - OPUS4-60365 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Advancing Digital Workflows in Materials Science: The Role of PMDco in Data Integration and Semantic Representation N2 - The field of Materials Science and Engineering (MSE) is undergoing a transformative shift towards digitalization, emphasizing the need for structured and interoperable data management. The Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, addresses these challenges by providing a robust mid-level semantic framework. PMDco bridges the gap between abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, and highly specific domain terminologies to ensure consistency and interoperability across diverse MSE applications. Developed through MSE community-based curation, PMDco facilitates the integration of real-world data from experiments, simulations, and industrial processes. This presentation will explore PMDco's role in enabling advanced digital workflows and its integration into demonstrators within the Platform MaterialDigital (PMD) initiative. Highlighted use cases include the semantic representation of tensile test data in compliance with ISO 6892-1:2019-11, utilizing the corresponding tensile test ontology (TTO) built on PMDco. Through an electronic laboratory notebook (ELN), data from experiments performed by undergraduate students were transformed into machine-actionable knowledge graphs, demonstrating the potential for education and fully digitalized experimental procedures. Additionally, a possible extension of PMDco as a linking point for semantically representing simulation data will be presented, aligning with the focus of VMAP. This includes linking experimental, simulation, and computational datasets to create comprehensive, FAIR-compliant knowledge ecosystems. By showcasing best practices in data acquisition, semantic integration, and knowledge graph generation, this presentation underscores PMDco’s versatility and its critical role in advancing digital MSE workflows. T2 - VMAP User Forum 2025 CY - Sankt Augustin, Germany DA - 18.02.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62607 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science through Semantic Technologies and Knowledge Graphs N2 - The field of materials science is undergoing a transformative shift driven by digitalization. In this respect, semantic and AI technologies are paving the way for advancements in materials development, design, and optimization while leaping towards an Industry 4.0 environment. Addressing the dual challenges of quality assurance and data interoperability, this presentation examines the integration of semantic technologies and knowledge representation methods. By adhering to FAIR principles, this approach enhances data management, storage, and reuse. That way, both machine-actionable and human-understandable data structures crucial for digital research environments are fostered. This presentation focuses on the ‘platform MaterialDigital’ (PMD) initiative, which aims to support efforts from both industrial and academic sectors to solve digitalization challenges and implement sustainable digital solutions. Besides establishing structures to create virtual material data spaces, PMD develops solutions for systematizing and unifying the handling of hierarchical, process-dependent material data. Semantic technologies play a crucial role in digitalization efforts as they enable the storage, processing, and querying of data in a contextualized form. Therefore, the development and prototypical application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. This includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. Its integration into daily lab life is demonstrated through its application to electronic lab notebooks (ELN). This illustrates potentials of standardized protocols and automation-ready solutions for managing diverse experimental data across different sources. Outlining best practices and illustrating the possibilities that semantic technologies bring to modern labs, examples from materials processing and mechanical testing will underscore how knowledge graphs bridge the gap between data and decision-making in materials science, with potential for increased productivity and streamlined workflows across the field. T2 - Materials Week 2025 CY - Frankfurt am Main, Germany DA - 03.04.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-62866 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Semantic Technologies for Digital Transformation in Materials Science: From PMDco to Prototypical Applications N2 - The digital transformation of Materials Science and Engineering (MSE) is accelerating the adoption of structured, interoperable, and FAIR data practices, in particular with respect to an advanced research data management. Semantic technologies play a pivotal role in this transformation, as the enable the integration, contextualization, and automation of diverse data sources across experimental, computational, and simulation domains. Central to these efforts is the Platform MaterialDigital Core Ontology (PMDco), now in version 3.0, which provides a robust mid-level semantic framework tailored for MSE. PMDco bridges abstract high-level ontologies, such as the Basic Formal Ontology (BFO) standardized in ISO/IEC 21838-2, with domain-specific terminologies to ensure consistency and interoperability across applications. This presentation explores the application of PMDco and its integration into workflows within the Platform MaterialDigital (PMD) initiative. Through its deployment in electronic laboratory notebooks (ELNs), PMDco enables semantic representation of experimental data, such as tensile test results compliant with ISO 6892-1:2019-11, transforming them into machine-actionable knowledge graphs. Prototypical implementations demonstrate how semantic technologies enhance laboratory processes, improve data reuse, and streamline documentation which offers opportunities for automation and education. Further extending its versatility, PMDco serves as a linking point for semantically representing simulation data, enabling comprehensive integration of experimental and computational datasets. This creates structured data spaces that support advanced digital workflows. Beyond PMDco, the presentation highlights the design of graph patterns and semantic shapes, showcasing generalizable methods for managing diverse data in MSE being based on data structuring and formatting. By presenting best practices in ontology development, data acquisition, and knowledge graph generation, this talk underscores the transformative potential of semantic technologies in MSE. It offers a forward-looking perspective on the role of structured data spaces as a driver for innovation, ensuring that materials science continues to advance through rigorous, interoperable, and automated digital methodologies. T2 - FEMS Euromat 2025 CY - Granada, Spanien DA - 14.09.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology KW - Graph Patterns PY - 2025 AN - OPUS4-64165 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Data-Driven Materials Science: Reproducibility and Standardization N2 - Advancing development and digitalization in materials science requires to focus on quality assurance, interoperability, and compliance with FAIR principles. Semantic technologies offer effective solutions for these challenges by enabling the storage, processing, and contextualization of data in machine-actionable and human-readable formats – essential for robust data management. This presentation highlights the PMD Core Ontology 3.0 (PMDco 3.0), developed specifically for the field of materials science and engineering, and its implementation within generic knowledge representation frameworks. Demonstrators such as standardized mechanical testing, material processing workflows, and the Orowan Demonstrator exemplify the ontology’s practical applications. The use of graph patterns, able to be compiled into rule-based semantic shapes, supports a unified and automated approach to managing heterogeneous experimental data across domains. T2 - Persson Group Seminar CY - Berkeley, CA, USA DA - 23.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology KW - Graph Patterns PY - 2025 AN - OPUS4-63484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - On Shared Vocabulary, Ontologies, Semantic Data and Prototype Applications N2 - The advancement of development and digitalization in materials science necessitates rigorous quality assurance, interoperability, and adherence to FAIR principles. Semantic technologies contribute to these objectives by facilitating the structured storage, processing, and contextualization of data, yielding machine-actionable and human-interpretable knowledge representations vital for modern data management. This presentation showcases the prototypical application of generic approaches of knowledge representation in materials science. It includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. The development and application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. Its integration into daily lab life is demonstrated through its functional incorporation into electronic lab notebooks (ELN). In this respect, a possible integration of semantic technologies into openBIS is presented. The openBIS system is usable as a central data storage system that may be complimented by semantic annotation of data to enhance data contextualization and automation. Graph-based knowledge representations and rule-based semantic shapes are shown which were developed alongside the PMD Core Ontology 3.0 (PMDco 3.0) and can enrich openBIS functionalities. Practical examples from material processing and mechanical testing illustrate how semantic extensions of openBIS enable machine-actionable, interoperable, and reusable research data, paving the way for a unified, ontology-driven laboratory data ecosystem. T2 - openBIS User Group Meeting (openBIS UGM) CY - Berlin, Germany DA - 22.09.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - OpenBIS KW - Ontologies KW - Graph Patterns PY - 2025 AN - OPUS4-64307 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Towards Structured Data Spaces: Prototypical Application of Semantic Technologies as a Driver for Innovation in Materials Science N2 - In the pursuit of advancing development and digitalization within materials science, ensuring quality assurance, interoperability, and adherence to FAIR principles is significant. To address these aspects, semantic technologies are employed for storage, processing, and contextualization of data, offering machine-actionable and human-readable knowledge representations crucial for data management. This presentation showcases the prototypical application of generic approaches of knowledge representation in materials science. It includes the design and documentation of graph patterns that may be compiled into rule-based semantic shapes. The development and application of the PMD Core Ontology 3.0 (PMDco 3.0) tailored for materials science is highlighted. Its integration into daily lab life is demonstrated through its functional incorporation into electronic lab notebooks (ELN). Examples of material processing and standardized mechanical testing illustrate how knowledge graph operations enhance ELN capabilities, providing a generalizable unified approach for managing diverse experimental data from different sources with automation potentials. T2 - TMS Specialty Congress 2025 CY - Anaheim, CA, USA DA - 15.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - Plattform MaterialDigital PY - 2025 AN - OPUS4-63401 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - FAIR data in PMD: Development of MSE mid-level and standard-compliant application ontologies N2 - The efforts taken within the project ‘platform MaterialDigital’ (PMD, materialdigital.de) to store FAIR data in accordance with a standard-compliant ontological representation (‘application ontology’) of a tensile test of metals at room temperature (ISO 6892-1:2019-11) will be presented. This includes the path from developing an ontology in accordance with the respective standard, converting ordinary data obtained from standard tests into the interoperable RDF format, up to connecting the ontology and data. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying. For further reusability of data and knowledge semantically stored, the PMD core ontology (PMDco) was developed, which is a mid-level ontology in the field of MSE. The semantic connection of the tensile test application ontology to the PMDco is also presented. Moreover, Ontopanel, a tool for domain experts facilitating visual ontology development and mapping for FAIR data sharing in MSE, is introduced briefly. T2 - World Congress on Integrated Computational Materials and Engineering (ICME) CY - Orlando, Florida, USA DA - 21.05.2023 KW - Digitalization KW - Semantic Web Technologies KW - FAIR KW - Data Interoperability KW - PMD Core Ontology KW - Tensile Test Ontology PY - 2023 AN - OPUS4-57549 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Seamless Science in Platform MaterialDigital (PMD): Demonstration of Semantic Data Integration as Good Practices N2 - Following the new paradigm of materials development, design, and optimization, digitalization is the main goal in materials sciences and engineering (MSE) 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 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. The project ‘platform MaterialDigital’ (PMD) aims to bring together and support interested parties from both industrial and academic sectors in a sustainable manner in solving digitalization tasks and implementing digital solutions. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. Core points to be dealt with are the development of agreements on data structures and interfaces implemented in distinct software tools and to offer users specific support in their projects. Furthermore, the platform contributes to a standardized description of data processing methods in materials research. In this respect, selected MSE methods are semantically represented on a prototypical basis which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data. Accordingly, this poster presentation illustrates demonstrators developed and deployed within the PMD project. Semantically anchored using the mid-level PMD Core Ontology (PMDco), they address data transformation leading to a novel data management which is based on semantic integrated data. The PMD data acquisition pipeline (DAP), which is fueled by traditional, diverse data formats, and a pipeline applying an electronic laboratory notebook (ELN) as data source are displayed. Additionally, the efficient combination of diverse datasets originating from different sources is demonstrated by the representation of a use case dealing with the well-known Orowan relation. T2 - Materials Science and Engineering Congress 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Demonstrators PY - 2024 AN - OPUS4-61136 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kostenko, Yevgen T1 - Harmonizing Viscoplastic Material Model Application within the BMBF-Project “DigitalModelling” of the Platform Material Digital- Basic Idea, General Strategy and Current Status N2 - For decades, Germany stands for excellent cutting-edge research in the field of so-called higher-value constitutive visco-plastic material models and can draw on a large and globally unique pool of material data. However, both the data and the model structure are extremely heterogeneous and sometimes fundamentally different from research center to research center and from industrial partner to industrial partner. To address the heterogeneity in the material model landscape appropriately, an adaptable material model for the specific application and the specific material is required. The relevant parameters for the adapted material model must be identified as objectively and automatically as possible. To achieve a potentially real-time capable implementation, the material model equation system should be abstracted. The “DigitalModeling” project, organized within the German Platform initiative Material Digital, aims to create a standard and an interface that harmonize the scientific and technical development of constitutive, visco-plastic material models, increase their visibility and maximize the productivity of future research funding. This presentation summarizes the basic idea, the strategy behind it as well as the current status of the project, which was started beginning of 2024. T2 - vgbe Workshop with Technical Exhibition Materials & Quality Assurance CY - Bergen, Norway DA - 07.05.2025 KW - Visco-plastic Material Model KW - Simulation Workflows KW - Ontologies KW - Digitalization PY - 2025 AN - OPUS4-64043 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 - CONF A1 - Schilling, Markus T1 - Semantic Technologies in Action: Integrating Mechanical and Microstructure Data in MSE N2 - Semantic technologies (ST) are a powerful tool for storing, processing, and querying data in a contextualized and interoperable manner. They enable machine-actionable and human-readable knowledge representations essential for advanced data management, retrieval, and reuse. As one of the key factors within the frame of the collaborative project platform MaterialDigital (PMD), the establishment of a virtual material data space and the semantic modeling of hierarchical, process-dependent material data is aimed at to serve as best-practice examples of knowledge representation through ontologies and knowledge graphs. In this presentation, the application of ST to a specific use case from the field of materials sciences and engineering (MSE) is demonstrated: the integration and analysis of data related to a 2000 series age-hardenable aluminum alloy. By semantically representing mechanical and microstructural data obtained from tensile tests and dark-field transmission electron microscopy across various aging times, an expandable knowledge graph was constructed that is aligned with the PMD Core Ontology (PMDco) and enriched through the Tensile Test (TTO) and Precipitate Geometry Ontologies. This semantically integrated dataset enables advanced analytical capabilities via SPARQL queries and reveals microstructure–property relationships consistent with the well-known Orowan mechanism. The approach highlights the potential of semantic data integration to support FAIR data principles and to foster a more data-centric and interoperable research infrastructure in MSE. T2 - MSE Research Data Forum 2025 CY - Siegburg, Germany DA - 08.07.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability PY - 2025 AN - OPUS4-63666 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Platform MaterialDigital (PMD) - Knowledge Representation, Interoperability, Reliability N2 - Through collaboration in the materials science and engineering (MSE) community, the PMD is collectively developing common standards and prototype solutions for data acquisition, structuring, storage, and processing. This comprises the development of architectural, standardized and foundational ontologies and workflows. Demonstrators resulting from these developments provide good practice examples. The PMD Core Ontology (PMDco) was created as an anchor that allows implementation of semantic data integration. It provides a common and expanding vocabulary to represent and share knowledge while enabling efficient collaboration and promoting interoperability between diverse domains. T2 - Kupfer-Symposium 2025 CY - Schwäbisch-Gmünd, Germany DA - 12.11.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2025 AN - OPUS4-64708 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 - 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 - Beygi Nasrabadi, Hossein T1 - Platform MaterialDigital (PMD) approach for the integration and management of FAIR low cycle fatigue (LCF) testing data N2 - This study represents the generation and storage of findable, accessible, interoperable, and reusable (FAIR) fatigue testing data by utilizing the Platform MaterialDigital (PMD) core ontology (PMDco) as well as some containerized PMD-server applications. Based on the specifications of the ISO 12106:2017-03 standard [1] and the acquired test reports of the mechanical testing facility, a highly comprehensive process graph of the fatigue testing procedure was created. Consequently, the PROV Ontology (PROVO) and PMDco [2] were used as upper-level ontologies to model the fatigue testing ontology (FTO). A part of the FTO classes hierarchy is shown in Fig. 1, where all the concepts of test procedure, test apparatus, test piece, and test properties were respectively located in the appropriate hierarchies of pmd:Process (prov:Activity), pmd:ProcessingNode (prov:Agent), pmd:Object (prov:Entity), and pmd:ValueObject (prov:Entity) classes. FTO is publicly available via the GitLab repository [3]. Reusing these upper-level ontologies and materials testing standards not only improves FTO's compatibility with other ontologies but also ensures its acceptance and deployment in industry [4]. The fatigue testing process graph has also been designed in such a manner that it allows for the entire mapping of testing metadata. In this respect, low-cycle fatigue (LCF) experiments were carried out on several cast copper alloys at various strain ratios, and the resulting CSV test report files were stored in a public repository [5]. Eventually, the processes of mapping the experimental test data into the fatigue process graph, converting the RDF data, storage of the triples in a triple store, and SPARQL query from the obtained triples are evaluated by different PMD-based tools like PMD OntoDocker [6]. T2 - MSE2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - Digitalization KW - FAIR KW - Low cycle fatigue PY - 2024 AN - OPUS4-61151 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Beygi Nasrabadi, Hossein 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. T2 - ICMDA 2024: 7th International Conference on Materials Design and Applications CY - Tokyo, Japan  DA - 09.04.2024 KW - Digitalization KW - Tensile stress relaxation KW - Ontology KW - Mechanical testing KW - Semantic web PY - 2024 AN - OPUS4-59979 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Seamless Science: Navigating Daily Lab Life through Semantic Data Integration, Electronic Lab Notebooks, and Knowledge Graphs in the Era of MSE Digitalization N2 - The field of materials science and engineering (MSE) is currently experiencing a significant paradigm shift towards enhanced digitalization which imposes a huge challenge on researchers, scientists, engineers, and technicians. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web technologies (SWT) are used as they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use. The collaborative project ‘platform MaterialDigital’ (PMD) aims to solve digitalization tasks and implement digital solutions in the field of MSE in a prototypical manner. Therefore, the establishment of a virtual material data space and the systematization of the handling of hierarchical, process-dependent material data are focused. In this respect, selected MSE methods are semantically represented which are supposed to serve as best practice examples with respect to knowledge representation and the creation of knowledge graphs used for material data. Accordingly, this presentation shows the efforts taken within PMD to store data in accordance with a testing standard compliant semantic representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). A semantic link of the corresponding tensile test ontology (TTO) and data leads to enhanced data useability and interoperability. The PMD core ontology (PMDco), developed in PMD and used as mid-level ontology in TTO, is also presented briefly. Moreover, as a best practice example, the acquisition of tensile test data with subsequent generation of knowledge graph data by semantic interconnection using TTO was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by undergraduate MSE students at the Technical University of Darmstadt. The resulting data pipeline, also illustrated in the presentation, enabled a fully-fledged digitally integrated experimental procedure, the approach of which may be transferred to other test series and experiments. In addition to facilitating the acquisition, analysis, processing, and (re)usability of data, this also raises the awareness of students with respect to data structuring and semantic technologies in terms of education and training. T2 - Materials Science and Engineering Congress 2024 CY - Darmstadt, Germany DA - 24.09.2024 KW - FAIR KW - Plattform Material Digital KW - Digitalization KW - Data Interoperability KW - Electronic Lab Notebook KW - Education PY - 2024 AN - OPUS4-61138 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - A Self-Driving Lab for Nano and Advanced Materials Synthesis in a Self-Driving Lab N2 - In this contribution, we present our Self-Driving Lab (SDL) for Nano and Advanced Materials, that integrates robotics for batched autonomous synthesis – from molecular precursors to fully purified nanomaterials – with automated characterization and data analysis, for a complete and reliable nanomaterial synthesis workflow. By fully automating the processing steps for seven different materials from five representative, completely different classes of nano- and advanced materials (metal, metal oxide, silica, metal organic framework, and core–shell particles) that follow different reaction mechanisms, we demonstrate the great versatility and flexibility of the platform. The system also exhibits high modularity and adaptability in terms of reaction scales and incorporates in-line characterization measurement of hydrodynamic diameter, zeta potential, and optical properties (absorbance, fluorescence). We discuss the excellent reproducibility of the various materials synthesized on the platform in terms of particle size and size distribution, and the adaptability and modularity that allows access to a diverse set of nanomaterial classes. We also present several key aspects of the central backend that orchestrates the (parallelized) syntheses workflows. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on a common description or ontology for representing the process steps and parameters of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. T2 - Series on Digitalisation-Meet the Experts | Special Topic: Automation CY - Berlin, Germany DA - 27.02.2026 KW - Self-Driving Labs KW - Materials Acceleration Platforms KW - Advanced Materials KW - Nanomaterials KW - Automation KW - Digitalization PY - 2026 AN - OPUS4-65604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -