5 Werkstofftechnik
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Organisationseinheit der BAM
- 5 Werkstofftechnik (71) (entfernen)
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.
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.
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.
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.
Laser Powder Bed Fusion (PBF-LB/M) of AISI 316L stainless steel has gained popularity due to its exceptional capacity to produce complex geometries and hierarchical microstructures, which can increase the yield strength while maintaining good ductility. Nevertheless, owing to high thermal gradients encountered during the process, the as printed 316L stainless steel often exhibit microstructural heterogeneities and residual stresses, which can limit its performance in demanding environments. Hence, employing heat treatments which balance the reduction of residual stresses while retaining improved static strength may be beneficial in various scenarios and applications. This study investigates the impact of post-processing heat treatments on the microstructure of 316L stainless steel manufactured via PBF-LB/M, along with its correlation with micro-hardness properties. To this end, 6 different heat treatments, i.e., 450 °C for 4h, 700 °C for 1h, 700 °C for 3h, 800 °C for 1h, 800 °C for 3h, and 900 °C for 1h, were applied to different specimens and Vickers hardness measurements (HV1) were performed in all states. At 800 °C, although the cellular structure appears to be retained, there is an observable increase in cellular size. However, while treatments exceeding 900 °C indicate no significant grain growth compared to other conditions, the cellular structure is entirely dissolved, which leads to a reduced Vickers hardness. The effect of the heat treatments on other microstructural features such as grain size and morphology, melt pool boundaries (MPB), crystallographic texture, chemical segregation, dispersoids and phase stability are also discussed in the present work
Es wird eine Studie zur Charakterisierung eines anisotropen Stahls vorgestellt, bei der Ultraschalluntersuchungen mit Mikrostrukturanalysen verbunden werden. Das Material weist hohe Festigkeit und Korrosionsbeständigkeit auf, zugleich ist mit anisotropen Eigenschaften die mechanischen und betrieblichen Eigenschaften beeinflussen zu rechnen. Vorläufige Ergebnisse lassen vermuten, dass weitere Untersuchungen notwendig sind, um die Fähigkeiten und Grenzen des Materials genau zu bestimmen. Es wird ein systematischer Ansatz mit Array- Prüfköpfen, Time-of-Flight Diffraction (TOFD) Technik und mikrostrukturellen Untersuchungen angewendet, um die Wechselwirkung zwischen Anisotropie und Mikrostruktur des Stahls zu analysieren. Ultraschallprüfungen mit der TOFD-Technik und in Tauchtechnik liefern Einblicke in das anisotrope Verhalten des Werkstoffes, einschließlich entsprechenden Kornorientierung, Dämpfung und Schallgeschwindigkeitsvariation. Diese Messungen führen in Verbindung mit mikrostrukturellen Analysen zu einem tieferen Verständnis des Materialverhaltens. Unser Hauptziel ist es, ein Framework zu erstellen, welches die Ultraschallantwort anisotroper Materialien mit ihren mikroskopischen Struktureigenschaften verbindet. Die vorgestellte Methodik ermöglicht eine zerstörungsfreie und zügige Bewertung der Materialintegrität, was besonders bei der Anwendung von Hochleistungsmaterialien relevant ist. Durch diesen integrativen Ansatz werden verschiedener Charakterisierungsmethoden kombiniert, um ein umfassenderes Materialverständnis zu erreichen.
Several studies have been shown that the electron beam can be used to create nanomaterials from microparticle targets in situ in a transmission electron microscope (TEM). Here, we show how this method has to be modified in order to synthesize plasmonic gold nanoparticles (NPs) on insulating silicon oxide substrate by employing a scanning electron microscope with a comparatively low acceleration voltage of 30 kV. The synthesized NPs exhibit a random distribution around the initial microparticle target: Their average size reduces from 150 nm to 3 nm with growing distance to the initial Au microparticle target. Similarly, their average distance increases. The synthesized NP assemblies therefore show distinctly different plasmonic behaviour with growing distance to the target, which allows to study consequences of random hybridization of surface plasmon in disordered system, such as Anderson localization. To reveal the surface plasmons and their localization behaviour we apply electron energy loss spectroscopy in the TEM.
The characterization of really unknown phases typically uses 70 to 150 reflectors for lattice metric calculation. The determination of the lattice parameters follows with 4% accuracy. Including a Z correction up to 1% can be reached. The precision of the lattice parameters ratios (a:b:c) is, however, better than 0.1%.
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, https://materialdigital.de) 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 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 towards the digitalization in MSE such as the development of the mid-level PMD core ontology (PMDco, https://github.com/materialdigital/core-ontology). Furthermore, selected results of a PMD partner project use case addressing data and knowledge management from synthesis, production, and characterization of materials are shown.
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.
The heat-resistant cast iron EN-GJSA-XNiSiCr35-5-2 (Ni-Resist D-5S) was investigated for its fatigue crack growth behavior at room and high temperatures. Force-controlled tests were carried out at constant temperatures (20 °C, 500 °C, 700 °C) without and with hold time and different load ratios. The crack growth behavior was also characterized under TMF loading (Tmin = 400 °C, Tmax = 700 °C) by applying IP and OP conditions and different load ratios. Three different techniques were combined to monitor crack growth: potential drop, thermography, and compliance method. The effect of the different loading conditions on the fatigue crack growth behavior will be presented and discussed.
Fatigue test ontology (FTO)
(2024)
Fatigue Test Ontology (FTO) has developed for representing the fatigue testing process, testing equipment requirements, test pieces charactristics, and related testing parameters and their measurement procedure according to DIN EN ISO 12106 standard.
Versions info:
V2 developed using PROVO+PMDco top-level ontologies.
V3 developed using BFO+IOF top-level ontologies.
Repositories:
GitLab: https://gitlab.com/kupferdigital/process-graphs/lcf-test
GitHub: https://github.com/HosseinBeygiNasrabadi/Fatigue-Test-Ontology-FTO-
MatPortal: https://matportal.org/ontologies/FTO
IndustryPortal: https://industryportal.enit.fr/ontologies/FTO
The damage mechanisms of metallic components produced by process laser powder bed fusion differ significantly from those typically observed in conventionally manufactured variants of the same alloy. This is due to the unique microstructures of additively manufactured materials. Herein, the focus is on the study of the evolution of creep damage in stainless steel 316L specimens produced by laser powder bed fusion. X-ray computed tomography is used to unravel the influence of the process-specific microstructure from the influence of the initial void distribution on creep damage mechanisms. The void distribution of two specimens tested at 600 °C and 650 °C is analyzed before a creep test, after an interruption, and after fracture. The results indicate that the formation of damage is not connected to the initial void distribution. Instead, damage accumulation at grain boundaries resulting from intergranular cracking is observed.
This is the stable version of the full-notch creep test ontology (OntoFNCT) that ontologically represents the full-notch creep test. OntoFNCT has been developed in accordance with the corresponding test standard ISO 16770:2019-09 Plastics - Determination of environmental stress cracking (ESC) of polyethylene - Full-notch creep test (FNCT).
The OntoFNCT provides conceptualizations that are supposed to be valid for the description of full-notch creep tests and associated data in accordance with the corresponding test standard. By using OntoFNCT for storing full-notch creep 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 is meant to 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 OntoFNCT facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of full-notch creep test data is enhanced and querying in combination with other aspects and data within the broad field of materials science and engineering (MSE) is facilitated.
The class structure of OntoFNCT forms a comprehensible and semantic layer for unified storage of data generated in a full-notch creep test including the possibility to record data from analysis and re-evaluation. Furthermore, extensive metadata allows to assess data quality and reliability. Following the open world assumption, object properties are deliberately low restrictive and sparse.
Fully automated and decentralized fused filament fabrication of ceramics for remote applications
(2024)
Manufacturing of ceramic components 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 ceramic components with complex geometries. 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. This poster presents an innovative approach: 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 parts are printed onto thin sacrificial rafts, which are built using an interface material with the same binder as the ceramic 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 of the ceramic parts and easy removal of the finished parts. In conclusion, our integrated approach enables significant advancements in the fabrication of complex ceramic components in remote environments with increased efficiency and minimal human handling.
A giant Zn segregation transition is revealed using CALPHAD-integrated density-based modeling of segregation into Fe grain boundaries (GBs). The results show that above a threshold of only a few atomic percent Zn in the alloy, a substantial amount of up to 60 at.% Zn can segregate to the GB. We found that the amount of segregation abruptly increases with decreasing temperature, while the Zn content in the alloy required for triggering the segregation transition decreases. Direct evidence of the Zn segregation transition is obtained using high-resolution scanning transmission electron microscopy. Base on the model, we trace the origin of the segregation transition back to the low cohesive energy of Zn and a miscibility gap in Fe-Zn GB, arising from the magnetic ordering effect, which is confirmed by ab-initio calculations. We also show that the massive Zn segregation resulting from the segregation transition greatly assists with liquid wetting and reduces the work of separation along the GB. The current predictions suggest that control over Zn segregation, by both alloy design and optimizing the galvanization and welding processes, may offer preventive strategies against liquid metal embrittlement.