5 Werkstofftechnik
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Organisationseinheit der BAM
- 5.2 Metallische Hochtemperaturwerkstoffe (17) (entfernen)
Despite of the significant advances in additive manufacturing (AM) process optimization there is still a lack of experimental results and understanding regarding the mechanical behavior and its relationship with the microstructural features of AM-parts, especially in loading conditions typical for safety-relevant applications. Within the scope of the presented ongoing investigations, a basic microstructural characterization, tensile tests at room and elevated temperature (400°C) as well as a characterization of the fatigue behavior of additively manufactured Ti-6Al-4V in the low cycle fatigue regime are carried out in the as-built state. After failure, different techniques are used to describe the failure mechanisms of the specimens. The AM-Specimens are provided by the Fraunhofer institute for production systems and design technology and investigated at the BAM following the philosophy of the TF-Project AGIL.
The PMD Core Ontology (PMDco) is a comprehensive set of building blocks produced via consensus building. The ontological building blocks provide a framework representing knowledge about fundamental concepts used in Materials Science and Engineering (MSE) today. The PMDco is a mid-level ontology that establishes connections between narrower MSE application ontologies and domain neutral concepts used in already established broader (top-level) ontologies. The primary goal of the PMDco design is to enable interoperability between various other MSE-related ontologies and other common ontologies.
PMDco’s class structure is both comprehensive and extensible, rendering it an efficient tool to structure MSE knowledge. The PMDco serves as a semantic middle-layer unifying common MSE concepts via semantic mapping to other semantic representations using well-known key terms used in the MSE domain. The PMDco enables straight-forward documentation and tracking of science data generation and in consequence enables high-quality FAIR data that allows for precise reproducibility of scientific experiments.
The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the production, derivation, and attribution of resources. Via this foundation, the PMDco enables the integration of data from various data origins and the representation of complex workflows.
In summary, the PMDco is a valuable advancement for researchers and practitioners in MSE domains. It provides a common MSE vocabulary to represent and share knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless tracing of science data. Overall, the PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain in general.
Virtual-lab-based determination of a macroscopic yield function for additively manufactured parts
(2019)
This work aims for a yield function description of additively manufactured parts of S316L steel at the continuum-mechanical macro-scale by means of so-called virtual experiments using a crystal plasticity (CP) model at meso-scale. Additively manufactured parts require the consideration of the specific process-related microstructure, which prevents this material to be macroscopically treated as isotropic, because of crystallographic as well as topological textures. From virtual experiments, yield loci under various loading conditions are simulated. The scale bridging from meso- to macro-scale is realised by the identification of the simulated yield loci as a modified anisotropic Barlat-type yield model representation.
In Germany spent nuclear fuel (SNF) and high level radioactive waste (HLW) are stored in interim storage containers with double lid systems. Those lids are equipped with metal seals (e.g. Helicoflex®) that ensure the safe enclosure of the inventory. Being licensed for up to 40 years of interim storage the evaluation of the long-term behavior of the seals is necessary, taking into account storage conditions, decay heat and possible mechanical loads.
The influence of prestraining on the aging response of an Al-Cu-Li alloy is investigated by preparation of different strain states (3 %, 4 %, 6 %) of the initial aging state. The Brinell hardness of the subsequently aged samples (up to 60 h aging time) was measured and it was found that the increasing dislocation concentration in the 3 different initial states leads to faster hardness increases and slightly higher maximum hardness.
The influence of heat treatment and creep loading on the microstructure of an Al-Cu-Li alloy was investigated. Especially the formation of different precipitates (T1 and Theta') were characterized and the microstructural changes under different ageing conditions (with and without external strain) were investigated to determine the effect od stress on the ageing process.
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) 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 on a prototypical basis 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 poster presentation illustrates demonstrators developed and deployed within the PMD project. Semantically anchored using the mid-level PMD Core Ontology (PMDco), they address data transformation leading to a novel data management which is based on semantic integrated data. The PMD data acquisition pipeline (DAP), which is fueled by traditional, diverse data formats, and a pipeline applying an electronic laboratory notebook (ELN) as data source are displayed. Additionally, the efficient combination of diverse datasets originating from different sources is demonstrated by the representation of a use case dealing with the well-known Orowan relation.
Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific.
In this poster presentation, an approach how to maintain a comprehensive and intuitive MSE-centric terminology composing a mid-level ontology–the PMD core ontology (PMDco)–via MSE community-based curation procedures is shown.
The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications.