TY - CONF A1 - Koch, Claudia T1 - Eine moderne Qualitätsinfrastruktur: digital und vernetzt N2 - Überblick über die digitale Transformation der Qualitätsinfrastruktur (QI), die Initiative QI-Digital sowie die Aktivitäten der BAM. T2 - 11. Sitzung DGZfP UA Schnittstellen, Dokumentation, Datensouveränität Speicherung, Archivierung CY - Saarbrücken, Germany DA - 11.4.2024 KW - Qualitätsinfrastruktur KW - Qualitätssicherung KW - Zerstörungsfreie Prüfung KW - digitale Transformation KW - Smart Standard KW - Digitales Zertifikat KW - Quality-X PY - 2024 AN - OPUS4-60347 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hübner, Martin A1 - Kromm, Arne A1 - Kannengießer, Thomas A1 - Dittmann, Florian A1 - Varfolomeev, Igor T1 - Eigenspannungsabbau mittels LTT – Schweißzusätzen unter Berücksichtigung der Nahtgeometrie N2 - Low Transformation Temperature (LTT)-Schweißzusätze sind ein innovativer Ansatz, um Eigenspannungen in Schweißnähten zu reduzieren. Dieser Vortrag behandelt neben den Einfluss der LTT-Schweißzusätze auch deren Naht-Ausführungen. Die unterschiedlichen LTT-Nahtgeometrien haben einen signifikanten Einfluss auf den Eigenspannungsabbau in der Schweißnaht und der Wärmeeinflusszone (WEZ). T2 - SFB 1120 Summer School CY - Steinfeld, Germany DA - 11.06.2024 KW - LTT (Low Transformation Temperature) KW - Eigenspannungen KW - Ermüdungsfestigkeit KW - Aufmischung PY - 2024 AN - OPUS4-60306 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bayerlein, Bernd A1 - Schilling, Markus A1 - Birkholz, Henk T1 - Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD) 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 on Digital Transformation CY - Gaithersburg, MD, USA DA - 11.06.2024 KW - Digital Transformation KW - Research Data Management KW - Ontology KW - Reusability KW - FAIR PY - 2024 AN - OPUS4-60381 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - Birkholz, H. 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 - Waurischk, Tina A1 - Müller, Ralf A1 - Reinsch, Stefan A1 - de Camargo, Andrea A1 - Contreras, A. A1 - Niebergall, R. A1 - Schottner, G. A1 - Kilo, M. A1 - Diegeler, A. A1 - Kempf, S. A1 - Puppe, F. A1 - Arendt, F. A1 - Chen, Y.-F. A1 - Sierka, M. A1 - Limbach, R. A1 - Pan, Z. A1 - Wondraczek, L. A1 - Gogula, S. A1 - Bornhöft, H. A1 - Deubener, J. T1 - Digital infrastructure 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 environment is intended to overcome these problems in future. For this purpose, a robotic high throughput glass melting system is equipped with novel 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. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples. All infrastructural parts were developed by a consortium consisting of the Fraunhofer ISC in Würzburg, the Friedrich-Schiller-University Jena (OSIM), the Clausthal University of Technology (INW) and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) as part of a joint project of the German research initiative MaterialDigital. T2 - 97. Glass-Technology Conference CY - Aachen, Germany DA - 27.05.2024 KW - Glass KW - Robotic melting KW - Ontologie KW - Simulation KW - Workflow KW - Data Space KW - Digital Twin PY - 2024 AN - OPUS4-60387 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Hertwig, Andreas A1 - Ermilova, Elena A1 - Hülagü, Deniz T1 - Determining Material Properties with Spectroscopic Ellipsometry N2 - In this lecture, an introduction will be given on Spectroscopic Ellipsometry, what quantities can be obtained with it, and how we use it in ELENA and other projects to determine functional parameters of thin layers at the nanoscale. T2 - Summer school ELENAM : metrology at the nanoscale CY - Fréjus, France DA - 02.06.2024 KW - Thin Layers KW - Ellipsometry KW - Nanotechnology KW - Electrical Paramters PY - 2024 AN - OPUS4-60247 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Chemical Understanding with Bonding Analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed. T2 - Otaniemi Center for Atomic-scale Materials Modeling Seminar CY - Aalto, Finland DA - 11.03.2024 KW - Automation KW - Materials Design KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with bonding analysis N2 - Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed. T2 - Seminar of the Department of Chemistry at Imperial College London CY - Online meeting DA - 20.02.2024 KW - Automation KW - Magnetism KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59546 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Unger, Jörg F. T1 - Coupling of structural and material design N2 - The presentations discusses a use case for the optimization of concrete structures where structural and material design are integrated in a computational workflow. The workflow is based on both physics-based and data-based models and experimental data is used to calibrate/train these models with a specific focus on the integration of ucertainties. T2 - 2nd Technical Meeting of TG.SAG.2 CY - Hannover, Germany DA - 16.04.2024 KW - Coupling of structural and material design KW - Cement Hydration Model PY - 2024 AN - OPUS4-59999 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Falkenhagen, Jana A1 - Weidner, Steffen A1 - Epping, Ruben T1 - Coupling of chromatographic and spectrometric techniques for polymer characterization N2 - Coupling of chromatographic and spectrometric techniques for polymer characterization; focus topics: LCxMALDI-TOF-MS and UPLC x ESI-TOF-MS T2 - 16. Tagung des Arbeitskreises Polymeranalytik CY - Online meeting DA - 22.03.2022 KW - Liquid chromatography KW - Mass spectrometry KW - Polymers KW - Two-dimensional chromatography (2D-LC) PY - 2022 AN - OPUS4-54567 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -