5 Werkstofftechnik
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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.
BAM is currently building up a platform of novel nanoRMs relying on iron oxide nanoparticles of different shape, size and surface chemistry. Iron oxide was chosen as a core material because of its relevance to the material and life sciences.
As a first candidate of this series, we present cubic iron oxide nanoparticles with a nominal edge length of 8 nm. These particles were synthesized by thermal decomposition of iron oleate in high boiling organic solvents adapting well-known literature procedures. After dilution to a concentration suitable for electron microscopy (TEM and SEM) as well as for small-angle X-ray scattering (SAXS) measurements, the candidate nanoRM was bottled and assessed for homogeneity and stability by both methods following the guidelines of ISO 17034 and ISO Guide 35.
The particle sizes obtained by both STEM-in-SEM and TEM are in excellent agreement with a minimum Feret of 8.3 nm ± 0.7 nm. The aspect ratio (AR) of the iron oxide cubes were extracted from the images as the ratio of minimum Feret to Feret resulting in an AR of 1.18 for TEM to 1.25 for SEM. Alternatively, a rectangular bounding box was fitted originating from the minimum Feret and the longest distance through the particle in perpendicular direction. This led to AR values of 1.05 for TEM and 1.12 for SEM, respectively. The results confirm the almost ideal cubic shape.
Following the new paradigm of materials development, design and optimization, the digitalization of materials and processes is the main goal 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. This includes storage, processing and querying of data in a preferably standardized form, also addressing the incorporation of standardization bodies. To meet the challenge to contextualize material data in a way that is consistent with all stakeholders, all necessary information on the condition of the material including production and application-related changes have to be made available via a uniform, machine-readable description. For this purpose, ontologies are to be used since they allow for machine-understandable knowledge representations and semantic conceptualizations that are needed for data management and the digitalization in the field of materials science.
With respect to this currently ever-growing topic of integration and reuse of data and knowledge from synthesis, production and characterization of materials, this presentation shows the efforts taken within the project Platform MaterialDigital (PMD, materialdigital.de) to store tensile test data in accordance with a standard-compliant ontological representation. The includes the path from developing an ontology in accordance with the respective standard, converting ordinary and arbitrarily selected data gained from standard tests into the interoperable RDF format, up to connecting the ontology and data, respectively. Finally, such data can be queried from a triple store.
In the field of material science and engineering (MSE), most mechanical test methods are standardized which serves as a valid basis for ontology development. Therefore, the well-known tensile test of metals at room temperature (DIN EN ISO standard 6892-1:2019-11) was selected to be considered as one of the first use cases in PMD. This consideration within the PMD features both, the ontological representation of such a tensile test in accordance with the standard as well as exemplary data generation. The semantic connection of the ontology and data leads to interoperability and an enhanced ability of querying.
Ontologies that represent a map of the concepts and relationships between them, are becoming an effective solution for data standardization and integration of different resources in the field of materials science, as efficient data storage and management is the building block of material digitization. However, building a domain ontology is not a simple task. It requires not only a collaborative effort between ontologists and domain experts, but also the modeling approaches and tools play a key role in the process. Among all approaches, graphical representation of domain ontologies based on standard conceptual modeling languages is widely used because of its intuitiveness and simplicity. Various tools have been developed to realize this approach in an intuitive way, such as Protégé plugins and web visualization tools.
The Materials-open-Lab (MatOLab) project, which aims to develop ontologies and workflows in accordance with testing standards for the materials science and engineering domains, adopted a UML (Unified Modeling Language) approach based on the diagrams.net. It is a powerful, popular, open-source graphical editor. In practical case studies, however, many users’ needs could not be met, such as reusing ontology, conversion, and data mapping. Users must switch between different tools to achieve a certain step, and thereby invariably increase learning cost. The lack of validation also leads to incorrect diagrams and results for users who are not familiar with the ontology rules. To address these issues, we designed Ontopanel, a diagrams.net-based plugin that includes a set of pipeline tools for semantic modeling: importing and displaying protégé-like ontologies, converting diagrams to OWL, validating diagrams by OWL rules, and mapping data. It uses diagrams.net as the front-end for method modeling and Django as the back-end for data processing. As a web-based tool, it is very easy to expand its functionality to meet changing practical needs.
Towards Interoperability: Digital Representation of a Material Specific Characterization Method
(2022)
Certain metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminum alloys, the strengthening mechanism is based on the controlled formation of nanometer sized precipitates, which represent obstacles to dislocation movement. Precise tuning of the material structure is critical for optimal mechanical behavior in the application. Therefore, analysis of the microstructure and especially the precipitates is essential to determine the ideal parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in a first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and to quantify their relevant dimensions.
The present work aims at the digital representation of this material-specific characterization method. Instead of a time-consuming, manual image analysis, an automatable, digital approach is demonstrated. Based on DF-TEM images of different precipitation states of a wrought aluminum alloy, a modularizable digital workflow for quantitative precipitation analysis is presented. The integration of this workflow into a data pipeline concept will also be discussed. Thus, by using ontologies, the raw image data, their respective contextual information, and the resulting output data from the quantitative precipitation analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure the reproducibility of the data. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
The presented work is part of two digitization initiatives, the Platform MaterialDigital (PMD, materialdigital.de) and Materials-open-Laboratory (Mat-o-Lab).
The round robin test investigated the reliability users can expect for AlSi10Mg additive manufactured specimens by laser powder bed fusion through examining powder quality, process parameter, microstructure defects, strength and fatigue. Besides for one outlier, expected static material properties could be found. Optical microstructure inspection was beneficial to determine true porosity and porosity types to explain the occurring scatter in properties. Fractographic analyses reveal that the fatigue crack propagation starts at the rough as-built surface for all specimens. Statistical analysis of the scatter in fatigue using statistical derived safety factors concludes that at a stress of 36.87 MPa the fatigue limit of 107 cycles could be reached for all specimen with a survival probability of 99.999 %.
The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience.
Machine learning methods enables the quantitative determination of fracture characteristics and fracture mechanisms utilizing digitized expert knowledge [1]. Although the application of SE images provides promising results, additional information is required to obtain reliable solutions. As expected, BSE and 3D information helps to improve the classification (Fig. 1). But only a fast, widely integrated, and automated topography measurement can provide the required amount of referenced surface data for the application of machine learning methods.
To fulfil these requirements, topographical data are obtained from a BSE detector with four symmetric segments (4Q-BSE) using shape-from-shading technology [2]. Surface height calculation is performed live during image acquisition and provides immediate feedback in three dimensions. All available signals (SE, BSE and more if applicable) are recorded simultaneously together with the surface topography and stored in a multichannel data file. This guaranties the same geometrical reference for all data, which is required for further analysis (Fig. 2).
When applying machine learning methods to topographic data together with SEM images, topographic information must be provided as depth image. Consequently, a unique height scale is required for all applied data with different magnifications. This requires a calibrated height measurement, which is ensured with the integrated 3D calibration of the topographic acquisition and a dedicated calibration sample. Thus, a large number of data sets from different fracture samples was generated and used as training data for machine learning.
The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. In the IGF project "iFrakto", software is being developed that quantitatively determines fracture characteristics and fracture mechanisms utilizing digitized expert knowledge, machine learning, and standard 2D and topographical data from SEM imaging. Topographical data are obtained from 4QBSE detector using shape-from-shading technology. In the medium term, a software tool should provide knowledge-based suggestions for the evaluation of fracture surfaces in real time during SEM work or at subsequent evaluation. As a basis for this, round robins were carried out among fractographers in order to create a knowledge base, to query the practice-relevant requirements for such tools and to carry out first practical tests. Actual results are presented and the relevance of the evaluation strategy is evaluated.
Ziel von fraktographischen Untersuchungen ist die Auswertung von makroskopischen und mikroskopischen Bruchflächenmerkmalen und daraus resultierend die Bestimmung des Bruchmechanismus eines Bauteils aus einem Schadensfall. Die Grundlage für die Bewertung von Bruchmerkmalen stammt dabei aus zuvor durchgeführten eigenen Vergleichsversuchen und aus der Literatur. Eine fraktographische Analyse kann sehr aufwändig sein und setzt in jedem Fall erhebliches Erfahrungswissen voraus. Im IGF-Vorhaben „iFrakto“ wird Software entwickelt, die auf Basis von digitalisiertem Expertenwissen, Machine Learning und unter Zuhilfenahme von Topographie-Daten anhand von REM-Aufnahmen Bruchmerkmale und Bruchmechanismen quantitativ bestimmt, s. Abbildung. Solche Software soll mittelfristig direkt am REM oder bei der nachfolgenden Auswertung wissensbasierte Vorschläge für die Bewertung von Bruchflächen liefern. Als Grundlage hierfür wurden Ringversuche unter Fraktograph/inn/en durchgeführt, um die Wissensbasis zu schaffen, die praxisrelevanten Anforderungen an solche Software abzufragen und erste Praxistests durchzuführen. Die bisherigen Ergebnisse werden vorgestellt und die Relevanz der Auswertestrategie und unterschiedlicher Sensordaten bewertet.
Thermally stimulated interactions between silver and glass, that is, silver dissolution as Ag+ and precipitation as Ag0 were studied in two glass series of molar target composition xAg2O–(19 − x)Na2O–28ZnO–53B2O3 with x = 0, 0.1, 0.5, 5 and (19Na2O–28ZnO–53B2O3)+yAg2O with y = 0.01, 0.05. These act as model for low-melting borate glasses being part of metallization pastes. The occurrence of metallic silver precipitates in melt-quenched glass ingots demonstrated that silver dissolved only in traces (< 0.01 mol%) in the glasses. The dissolved silver was detected by means of Raman spectroscopy and energy-dispersive X-ray spectroscopy. Increasing x in the batch could not lead to a significant increase of the silver ion fraction in the glass as possible in binary silver borate glasses. In situ observation of heated AgNO3 mixed with the base glass frit in a hot stage microscope showed that Ag0 precipitation occurs already at the solid state. At higher temperatures, small droplets of liquid silver were found to move freely within the melt, whereas coalescence caused a stepwise increase of their size.
These results contribute to the understanding of formation of silver precipitates in metallization pastes described in the literature.