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Organisationseinheit der BAM
- 5 Werkstofftechnik (358) (entfernen)
Due to the advantages of Laser Powder Bed Fusion (PBF-LB), i.e., design freedom and the possibility to manufacture parts with filigree structures, and the considerable amount of knowledge available for 316L in its conventional variant, the mechanical behavior, and related microstructure-property relationships of PBF-LB/316L are increasingly subject of research. However, many aspects regarding the - application-relevant - mechanical behavior at high temperatures are not yet fully understood. Here, we present the results of an experimental study on the LCF behavior of PBF-LB/316L featuring a low defect population, which makes this study more microstructure-focused than most of the studies in the literature. The LCF tests were performed between room temperature (RT) and 600 °C. The mechanical response is characterized by strain-life curves, and hysteresis and cyclic deformation curves. The damage and deformation mechanisms are studied with X-ray computed tomography, and optical and electron microscopy. The PBF-LB/M/316L was heat treated at 450 °C for 4 h, and a hot‑rolled (HR) 316L variant with a fully recrystallized equiaxed microstructure was tested as a reference. Besides, selected investigations were performed after a subsequent heat treatment at 900 °C for 1 h. The PBF-LB/316L exhibits higher cyclic stresses than HR/316L for most of the fatigue life, especially at room temperature. At the smallest strain amplitudes, the fatigue lives of PBF-LB/M/316L are markedly shorter than in HR/316L. The main damage mechanisms are multiple cracking at slip bands (RT) and intergranular cracking (600 °C). Neither the melt pool boundaries nor the gas porosity have a significant influence on the LCF damage mechanism. The cyclic stress-strain deformation behavior of PBF-LB/M/316L features an initial hardening followed by a continuous softening. The additional heat treatment at 900 °C for 1 h led to decreased cyclic stresses, and a longer fatigue life.
The strength of age-hardenable aluminum alloys is based on the controlled formation of nm-sized precipitates, which represent obstacles to dislocation movement. Transmission electron microscopy (TEM) is generally used to identify precipitate types and orientations and to determine their size. This geometric quantification (e.g., length, diameter) is often performed by manual image analysis, which is very time consuming and sometimes poses reproducibility problems. The present work aims at the digital representation of this characterization method by proposing an automatable digital approach. Based on DF-TEM images of different precipitation states of alloy EN AW-2618A, a modularizable digital workflow is described for the quantitative analysis of precipitate dimensions. The integration of this workflow into a data pipeline concept is also presented. The semantic structuring of data allows data to be shared and reused for other applications and purposes, which enables interoperability.
The influence of test temperature and frequency on the fatigue life of the alloy EN AW-2618A (2618A) was characterized. The overaged condition (T61 followed by 1000 h/230 °C) was investigated in load-controlled tests with a stress ratio of R = -1 and two test frequencies (0.2 Hz, 20 Hz) at room temperature and at 230°C, respectively. An increase in the test temperature reduces fatigue life, whereby this effect is more pronounced at lower stress amplitudes. Decreasing the test frequency in tests at high temperatures further reduces the service life.
Optical glasses and glass ceramics are present in many devices often used in our daily routine, such as the mobile phones and tablets. Since the 1960´s with the development of glass lasers, and more recently, within the search for efficient W-LEDs, sensors and solar converters, this class of materials has experienced extreme research progress. In order to tailor a material for such applications, it is very important to understand and characterize optical properties such as refractive index, transmission window, absorption and emission cross sections, quantum yields, etc. These properties can often be tuned by appropriate compositional choice and post-synthesis processing. In this lecture we will discuss the optical properties of glasses and glass ceramics, relevant to that end.
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-understandable and human-readable knowledge representations needed for data management, retrieval, and (re)use.
The project ‘platform MaterialDigital’ (PMD) is part of an initiative that 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 added values 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 ontologically 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 the PMD project to store data in accordance with a testing standard compliant ontological representation of a tensile test of metals at room temperature (ISO 6892-1:2019-11). This includes the path from developing an ontology in accordance with the respective standard 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 enhanced reusability of data and knowledge from synthesis, production, and characterization of materials, the PMD core ontology (PMDco) was developed as mid-level ontology in the field of MSE. The semantic connection of the tensile test ontology (TTO) to the PMDco leads to enhanced expressivity and interoperability.
Moreover, as a best practice example, generation and acquisition of test data semantically connected to the ontology (data mapping) was realized by applying an electronic laboratory notebook (ELN). Corresponding tensile tests were performed by materials science students at university. This enabled a fully digitally integrated experimental procedure that can 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 the sense of education and training. The entire data pipeline is further seamlessly integrable in a laboratory information management system (LIMS). More specifically, the integration of semantic conceptualization and knowledge graphs may become essential parts in LIMS as this would be very beneficial. Therefore, some first approaches of SWT integration in LIMS will also be presented briefly.
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.
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.
Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD)
(2024)
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.
Gläser zeichnen sich durch eine breite und kontinuierlich abstimmbare chemische Zusammensetzung sowie einzigartige Formgebungstechniken aus, was sie oft zur Schlüsselkomponente moderner Hochtechnologien macht. Die Glasentwicklung ist jedoch oft noch zu kosten-, zeit- und energieintensiv. Der Einsatz von robotergestützten Schmelzsystemen, eingebettet in eine Ontologie-basierte digitale Umgebung, soll diese Probleme in Zukunft überwinden. Im Rahmen der BMBF Forschungsinitiative MaterialDigital unternimmt das Verbundprojekt GlasDigital „Datengetriebener Workflow für die beschleunigte Entwicklung von Glas“ erste Schritte in diese Richtung. Das Projektkonsortium, an dem das Fraunhofer ISC in Würzburg, die Friedrich-Schiller-Universität Jena (OSIM), die Technische Universität Clausthal (INW) und die Bundesanstalt für Materialforschung und -prüfung (BAM, Fachgruppe Glas) beteiligt sind, will alle wesentlichen Basiskomponenten für eine beschleunigte datengetriebene Glasentwicklung zusammenführen. Zu diesem Zweck wird ein robotergestütztes Hochdurchsatz-Glasschmelzsystem mit neuartigen Inline-Sensoren zur Prozessüberwachung, auf maschinellem Lernen (ML) basierenden adaptiven Algorithmen zur Prozessüberwachung und -optimierung, neuartigen Werkzeugen für die Hochdurchsatz-Glasanalyse sowie ML-basierten Algorithmen zum Glasdesign, Data Mining sowie Eigenschafts- und Prozessmodellierung ausgestattet. Der Vortrag gibt einen Überblick darüber, wie all diese Komponenten miteinander verzahnt sind, und veranschaulicht ihre Nutzbarkeit anhand einiger Beispiele.