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 - Waske, Anja T1 - A unique authenticator for additively manufactured parts derived from their microstructure N2 - In the field of additive manufacturing, the ability to uniquely identify and authenticate parts is crucial for certification, logistics, and anti-counterfeiting efforts. This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. Unlike traditional tagging methods, such as embedding QR codes on the surface [1] or within the volume of parts, this approach requires no alteration to the printing process, as it utilizes naturally occurring microstructural characteristics. The proposed workflow [2] involves the analysis of 3D micro-computed tomography data to identify specific voids that meet predefined identification criteria. This method is demonstrated on a batch of 20 parts manufactured with identical process parameters, proving capable of achieving unambiguous identification and authentication. By establishing a tamper-proof link between the physical part and its digital counterpart, this methodology effectively bridges the physical and digital realms. This not only enhances the traceability of additively manufactured parts but also provides a robust tool for integrating digital materials, parts databases, and product passports with their physical counterparts. T2 - Artificial Intelligence in MSE CY - Bochum, Germany DA - 18.11.2025 KW - Authentication KW - Additive Manufacturing KW - Non-destructive testing PY - 2025 AN - OPUS4-65204 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Singh, Shobhit T1 - Accelerated Creep Testing in High- Temperature Alloys N2 - This study introduces an innovative method for efficiently determining the creep properties of high-temperature materials through high-throughput testing, employing digital image correlation [1,2]. The focus is on the AlCoCrFeNiTi alloy [3-5], synthesized by directional solidification, known for its exceptional strength and unique properties. Experimental investigations, including compression, and bending creep tests, were conducted at 750°C. Capitalizing on the inhomogeneous stress and strain distribution within a cantilever subjected to bending, we leverage this characteristic to extract multiple creep curves from a single test. Stresses at key points were determined using existing analytical solutions [5,6]. Uniaxial tests spanning 300 to 500 MPa initial stress were complemented by bending tests designed to induce similar stress levels. A detailed comparison between bending and uniaxial creep is presented, including the results of verification studies on additional alloys. This methodology not only expedites testing but also minimizes material usage, energy consumption, and manual labour. This research showcases a reliable and time-efficient approach to exploring the creep behavior of high-temperature materials. The technique is particularly advantageous for characterizing precious alloys with limited dimensions. Microstructural heterogeneity may exist in specimens tested under bending load, however, it can still be correlated to the mechanical properties with modern high-resolution characterization methods. Stress and resulting strain can be directly compared in a single specimen, ensuring uniform manufacturing, and heating history. This method eliminates the possible errors due to testing with different rigs, which could impair the accuracy of studies based on individual tests. T2 - International Conference on Creep and Fracture of Engineering Materials and Structures CY - Bengaluru, KA, India DA - 28.07.2024 KW - Digital Image Correlation KW - Creep KW - High-Temperature Materials PY - 2024 AN - OPUS4-60925 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Adapting FAIR Practices in Materials Science: Digital Representation of Material-Specific Characterization Methods N2 - Age-hardenable aluminum alloys undergo precise heat treatments to yield nanometer-sized precipitates that increase their strength and durability by hindering the dislocation mobility. Tensile tests provide mechanical properties, while microstructure evaluation relies on transmission electron microscopy (TEM), specifically the use of dark-field TEM images for precise dimensional analysis of the precipitates. However, this manual process is time consuming, skill dependent, and prone to errors and reproducibility issues. Our primary goal is to digitally represent these processes while adhering to FAIR principles. Ontologies play a critical role in facilitating semantic annotation of (meta)data and form the basis for advanced data management. Publishing raw data, digital workflows, and ontologies ensures reproducibility. This work introduces innovative solutions to traditional bottlenecks and offers new perspectives on digitalization challenges in materials science. We support advanced data management by leveraging knowledge graphs and foster collaborative and open data ecosystems that potentially revolutionize materials research and discovery. T2 - TMS - Specialty Congress 2024 CY - Cleveland, Ohio, US DA - 16.06.2024 KW - FAIR KW - Research Data Management KW - Semantic Interoperability KW - Ontologies KW - Materials and Processes Data Reusability PY - 2024 AN - OPUS4-60375 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zocca, Andrea T1 - Additive Manufacturing for dental restorations by layerwise slurry deposition (LSD-print) technology N2 - The growing market of custom-made dental restorations offers a major potential for an application of ceramic additive manufacturing (AM). The possibility to individualize patient specific design and to establish new efficient workflows, from model generation to manufacturing, can be fully exploited by AM technologies. However, for mass customization to be truly envisioned, ceramic AM needs to achieve a level of maturity, aesthetic quality, and productivity comparable to established manufacturing processes. In this presentation, the potential of the “layerwise slurry deposition” LSD-print technology for dental applications will be explored. It has been shown in the past years that the LSD-print can be applied to advanced ceramic materials such as alumina and silicon-infiltrated silicon carbide. For these materials, the LSD-print technology combines the high-speed printing of binder jetting with the possibility of producing a variety of high-quality ceramics. The current development deals with the challenges of applying this technology to a feldspar dental material, comparing the quality of AM restorations with the equivalent material for an established CAD/CAM workflow. Preliminary results not only indicate that the AM material produced by LSD-print can be competitive in terms of mechanical properties, but also that aesthetically satisfactory restorations can be manufactured for veneers, inlays and onlays as well as single unit fixed dental prostheses (FDPs). The presentation focuses on the material and technological challenges alongside the process chain, from the printing process, to debinding, firing and finishing the restorations. T2 - XVIIIth Conference of the European Ceramic Society CY - Lyon, France DA - 02.07.2023 KW - Additive Manufacturing KW - Layerwise slurry deposition KW - dental KW - ceramic PY - 2023 AN - OPUS4-58469 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zocca, Andrea T1 - Additive Manufacturing of advanced ceramics by layerwise slurry deposition and binder jetting (LSD-print) N2 - Powder bed technologies are amongst the most successful Additive Manufacturing (AM) techniques. Powder bed fusion and binder jetting especially are leading AM technologies for metals and polymers, thanks to their high productivity and scalability. The application of these techniques to most ceramics has been difficult so far, because of the challenges related to the deposition of homogeneous powder layers when using fine powders. In this context, the "layerwise slurry deposition" (LSD) has been developed as a layer deposition method which enables the use of powder bed AM technologies also for advanced ceramic materials. The layerwise slurry deposition consists of the layer-by-layer deposition of a ceramic slurry by means of a doctor blade, in which the slurry is deposited and dried to achieve a highly packed powder layer. This offers high flexibility in the ceramic feedstock used, especially concerning material and particle size. The LSD technology can be combined with binder jetting to develop the so-called “LSDprint” process for the additive manufacturing of ceramics. The LSDprint technology combines the high-speed printing of binder jetting with the possibility of producing a variety of high-quality ceramics with properties comparable to those achieved by traditional processing. In this presentation, the LSD process will be introduced and several examples of application ranging from silicate to high-performance ceramics will be shown. Recent developments towards the scale-up and industrialization of this process will be discussed, alongside future perspectives for the multi-material additive manufacturing. T2 - Ceramics in Europe 2022 CY - Krakow, Poland DA - 10.07.2022 KW - Layerwise slurry deposition KW - Laser induced slipcasting KW - Additive Manufacturing KW - Ceramics PY - 2022 AN - OPUS4-55543 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zocca, Andrea T1 - Additive manufacturing of ceramics from water-based feedstocks with low binder content N2 - A major challenge in the current ceramic additive manufacturing is the debinding of the printed parts, which is often associated with long process times and limitations in size and wall thickness. Binder jetting, a powder-based AM technology, can in principle generate thick-walled parts with a low amount of organics and with high productivity. However, this technology is often not capable of handling the fine powders required as raw materials for most technical ceramics. In this context, the "layerwise slurry deposition" (LSD-print) has been developed as a layer deposition method using water-based ceramic slurry feedstocks, enabling binder jetting also for advanced ceramic materials. LSD-print thus combines the high-speed printing of binder jetting with the possibility of producing a variety of high-quality technical ceramics. Perspectives and limitations of the LSD-print will be presented, with focus on achieving short debinding cycles and high productivity. T2 - MS&T CY - Pittsburgh, PA, USA DA - 06.10.2024 KW - Additive Manufacturing KW - Ceramic KW - Binder KW - Slurry KW - Water-based PY - 2024 AN - OPUS4-61882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd T1 - Adopting FAIR data practices in materials science: Semantic representation of a quantitative precipitation analysis N2 - Many metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminium alloys, the strengthening mechanism is based on the controlled formation of nanometre-sized precipitates, which represent obstacles to dislocation movement and consequently increase the strength. Precise tuning of the material microstructure is thus crucial for optimal mechanical behaviour under service condition of a component. Therefore, analysis of the microstructure, especially the precipitates, is essential to determine the optimum parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in the first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and thereafter quantify their relevant dimensions. Often, these evaluations are still performed by manual image analysis, which is very time-consuming and to some extent also poses reproducibility problems. Our work aims at a semantic representation of an automatable digital approach for this material specific characterization method under adaption of FAIR data practices. Based on DF-TEM images of different precipitation states of a wrought aluminium alloy, the modularizable, digital workflow of quantitative analysis of precipitate dimensions is described. The integration of this workflow into a data pipeline concept will also be discussed. Using ontologies, the raw image data, their respective contextual information, and the resulting output data of the quantitative image analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure data reproducibility. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability. T2 - FEMS EUROMAT 2023 CY - Frankfurt a. M., Germany DA - 03.09.2023 KW - Semantic Representation KW - FAIR data management KW - Quantitative Precipitation Analysis KW - Knowledge graph and ontologies PY - 2023 AN - OPUS4-58199 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Trappe, Volker T1 - Advanced lightweight applications – recycling versus reliability and fossil energy footprint N2 - Advanced light weight applications like aircrafts and wind turbine blades are made of fibre reinforced plastics (FRP) with continuous fibre reinforcement and must withstand a high thermo-mechanical cyclic loading. The quality of the fibre matrix interface has a high impact on the fatigue life and was continuously improved over the years since the 50th. The fatigue life of glass fibre reinforced plastics (GFRP) used in aircraft industry is 10 to 100 times higher compared to glass fibre non crimp fabrics used for wind turbine blades. To assure a constant and reliable high quality and strength of reinforcement fibres, synthetic fibre production is state of the art (CF, GF). There is a need for recycling GFRP and CFRP waste due to the upcoming use. Pyrolysis and solvolysis are more expensive than the mechanical route however enable a more sustainable recycling. Natural fibres and recycled synthetic fibres have a high scatter in quality and strength. Hence it is a challenge to optimize the production / recycling processes to get a reliable quality for any demanding (second life) application. Chemical routes for using renewables resources and recycling, is going to be a good approach especially for polymer-matrix systems to get 100% quality (back) compared to the state of the art. Finally, a proper design, life-time extension and repair is preferable to recycling to keep the carbon footprint as low as possible. T2 - 27. INTERNATIONALES DRESDNER LEICHTBAUSYMPOSIUM CY - Dresden, Germany DA - 13.06.2024 KW - Polymer Matrix Composites KW - Carbon Fibre KW - Recycling KW - Circular Economy PY - 2024 UR - https://leichtbausymposium.de/deu/ AN - OPUS4-60683 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 -