TY - CONF A1 - Schilling, Markus T1 - Digital Transformation in Materials Science: PMD Core Ontology (PMDco) 3.0 – Patterns & Applications N2 - The digitalization of Materials Science and Engineering (MSE) demands standardized, interoperable approaches for representing complex experimental and simulation data. To address this challenge, the Platform MaterialDigital Core Ontology (PMDco) provides a mid-level semantic framework that bridges high-level ontologies and domain-specific terminologies. PMDco version 3.0, recently released, introduces significant enhancements based on a full alignment with the Basic Formal Ontology (BFO) as standardized in ISO/IEC 21838-2. This alignment ensures conceptual consistency and facilitates integration across heterogeneous data sources while enabling FAIR-compliant workflows. First addressing the idea behind a mid-level ontology in MSE, this presentation emphasizes its role in harmonizing diverse data models and supporting machine-actionable knowledge representation. New features and design patterns introduced in PMDco 3.0 will be highlighted, which strengthen interoperability and provide reusable modeling structures for common MSE concepts. Practical applications and prototype implementations will be discussed. PMDco has been developed through active community involvement and its future evolution relies on continued collaboration and discussion within the MSE community. Participation is strongly encouraged to ensure that PMDco remains relevant, comprehensive, and widely adopted. More information and opportunities to contribute can be found at materialdigital.de and github.com/materialdigital/core-ontology. T2 - VMAP User Forum 2026 CY - Sankt Augustin, Germany DA - 24.02.2026 KW - Semantic Technology KW - Knowledge Graph KW - PMD Core Ontology KW - Semantic Pattern KW - Machine-actionability PY - 2026 AN - OPUS4-65574 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Eddah, Mustapha T1 - Multi-Energy High Dynamic Range (HDR) Synchrotron X-ray Computed Tomography applied to LTCC samples N2 - Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-to-noise ratio (SNR). One example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35% improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. T2 - iCT 2026 CY - Linz, Austria DA - 10.02.2026 KW - Synchrotron x-ray CT KW - Multi-energy CT KW - Low-Temperature cofired ceramics PY - 2026 AN - OPUS4-65586 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 - 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 - Saber, Yassin A1 - Clague, Leighton T1 - Automated Fused Filament Fabrication of Ceramics and Metals - Remote and in Space N2 - Component manufacturing in remote (i.e., geographically isolated) settings poses significant challenges where access to conventional manufacturing facilities is limited or non-existent. Fused Filament Fabrication (FFF) enables the rapid manufacturing of plastic, metallic and ceramic components with complex geometries. Ceramic and metallic parts formed by FFF require subsequent debinding and sintering to reach full density. Debinding and sintering are typically executed in separate steps with different equipment, necessitating extensive human handling which hinders process automation and may be challenging for the operator in isolated environments. Here an innovative approach is presented: the integration of all process steps into a single, fully automated system, streamlining the process and minimizing human involvement. Our system combines a dual extrusion filament printer with a porous and heat-resistant ceramic print bed. The porous print bed enables mechanical interlocking of the first printed layers, ensuring adhesion and structural integrity during FFF. Ceramic and metallic parts are printed onto thin sacrificial rafts, which are built using an interface material with the same binder as the loaded filament. After the print is completed, the heat-resistant print bed with all parts is transferred seamlessly with a carrier system into a high-temperature furnace for debinding and sintering. During sintering the sacrificial raft is disintegrated, allowing for unconstrained sintering and easy removal of the finished parts. In conclusion, our integrated approach enables significant advancements in the fabrication of complex ceramic or metallic components in remote environments with increased efficiency and minimal human handling. T2 - AM Forum 2025 CY - Berlin, Germany DA - 17.03.2025 KW - Fused Filament Fabrication KW - Ceramics KW - Metalls KW - Process automation PY - 2025 AN - OPUS4-62745 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 - Rodricks, Carol T1 - Advancing Sustainable Composites: Challenges and innovations N2 - With the increased use of carbon fibre polymer matrix composites comes the important question of their management at the end of their life cycle. Given the high costs associated with carbon fibre production, recycling carbon fibres from composite waste is a desirable source of reinforcing fibres for new applications. However, current recycling methods result in recycled carbon fibres that are short with little to no orientation which can only be used in applications requiring intermediate strength at a fraction of the potential of the continuous, aligned virgin fibres. Thus, a method to recycle fibres with their original length and orientation intact is vital to truly realising a circular economy for carbon fibre polymer composites. Our research introduces a novel hierarchical composite aimed at preserving the length and orientation of carbon fibres on recycling. Virgin carbon fibres are encapsulated in an insoluble epoxy matrix to form tapes that serve as the primary units of the hierarchical structure. The primary epoxy matrix protects the fibres from chemical and environmental elements while maintaining their permanent orientation. The primary tape units are subsequently embedded in a secondary recyclable matrix polymer to make larger composite structures. Elium, a thermoplastic that dissolves in acetone and has mechanical properties comparable to epoxy, was chosen as the secondary matrix of choice in this study. This approach aims to achieve a composite that is mechanical equivalent to thermoset composites while facilitating easy recycling with minimal impact on the fibres in the primary unit. T2 - Materials Week 2025 CY - Frankfurt am Main, Germany DA - 02.04.25 KW - Recycling KW - Carbon fibres KW - Mechanical testing KW - Polymer-matrix composites (PMCs), micromechanics KW - Elium PY - 2025 AN - OPUS4-63004 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Nguyen, Ngoc Tam Thi T1 - Reinventing Piping for Hydrogen Applications – A Composite Approach N2 - A key step towards achieving climate neutrality in the European Union (EU) by 2050 is the transition to renewable energy sources. Green hydrogen plays a central role in this shift but remains costly, particularly through the utilization of PEM electrolysis. Currently, around 50% of the costs associated with electrolyser systems are attributed to system peripherals, underscoring the potential for cost savings through standardization and the use of cost-effective materials [Tja17, IRE20]. In the collaborative project "PolyH2Pipe", the Institute of Plastics Processing in Industry and Craft (IKV), the Welding and Joining Institute (ISF) and BAM Division 5.3 are developing media-carrying pipe systems made of continuous fiber-reinforced thermoplastics (TP-FR) for hydrogen applications. The project aims to design these pipes, develop suitable joining techniques and validate the requirements for these piping systems. This research initiative serves as a foundational basis for the subsequent market introduction of large-scale TP-FR pipe systems for electrolysers and fuel cell systems. Initial test results will be presented as a part of the talk during the colloquium. T2 - H2Safety Colloquium - Materials compatibility and properties CY - Online meeting DA - 14.05.2025 KW - Hydrogen KW - Polymer Matrix Composites KW - Electrolyser KW - Piping PY - 2025 AN - OPUS4-63134 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Ralf T1 - GlasDigital: Data-driven workflow for accelerated glass development N2 - Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital infrastructure might overcome these problems. As part of the German research initiative MaterialDigital [1], the joint project GlasDigital took first steps in this direction combining all main components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system was equipped with inline sensors for process monitoring, machine learning (ML)-based adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including data mining as well as property and process modelling. The talk gives an overview how these tools are interconnected and illustrates their usability. T2 - 27th International Congress on Glass 2025 CY - Kolkata, India DA - 20.01.2025 KW - Materials digitalisation PY - 2025 AN - OPUS4-63820 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 -