5 Werkstofftechnik
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Up to now, oriented surface crystallization phenomena are discussed controversially, and related studies are restricted to few glasses. For silicate glasses we found a good correlation between the calculated surface energy of crystal faces and oriented surface nucleation. Surface energies were estimated assuming that crystal surfaces resemble minimum energy crack paths along the given crystal plane. This concept was successfully applied by Rouxel in calculating fracture surface energies of glasses. Several oriented nucleation phenomena can be herby explained assuming that high energy crystal surfaces tend to be wetted by the melt. This would minimize the total interfacial energy of the nucleus. Furthermore, we will discuss the evolution of the microstructure and its effect on the preferred crystal orientation.
Up to now, oriented surface crystallization phenomena are discussed controversially, and related studies are restricted to few glasses. For silicate glasses we found a good correlation between the calculated surface energy of crystal faces and oriented surface nucleation. Surface energies were estimated assuming that crystal surfaces resemble minimum energy crack paths along the given crystal plane. This concept was successfully applied by Rouxel in calculating fracture surface energies of glasses. Several oriented nucleation phenomena can be herby explained assuming that high energy crystal surfaces tend to be wetted by the melt. This would minimize the total interfacial energy of the nucleus. Furthermore, we will discuss the evolution of the microstructure and its effect on the preferred crystal orientation.
Engineering grain boundaries demands a quantitative description of both their segregation and specific phase behavior. Recently I have proposed a density-based model for grain boundary thermodynamics that enables CALPHAD integrated derivation of grain boundary phase diagrams, broadly applied now in studying various alloys. Combining this model with experimental investigations, in this talk, new aspects of interfacial segregation and phase transformation revealed in polycrystalline alloys are discussed. The effect of elastic interaction on grain boundary phase behavior is incorporated. We consider Al alloys and novel high-entropy alloys and discuss a general strategy for grain boundary engineering.
The dataset contains experimental mechanical data from complex service-like creep-fatigue experiments performed isothermally at 620 °C and a low strain amplitude of 0.2 % on tempered martensite-ferritic grade P92 steel. The data sets in text file format provide cyclic deformation (min. and max. stresses) and the total (hysteresis) data of all recorded fatigue cycles for three different creep-fatigue experiments: 1) a standard relaxation fatigue (RF) test with symmetrical dwell times of three minutes introduced at minimum and maximum strain, 2) a fully strain-controlled service-like relaxation (SLR) test combining these three-minute peak strain dwells with a 30-minute dwell in between at zero strain, and 3) a partly stress-controlled service-like creep (SLC) test combining the three-minute peak strain dwells with 30-minute dwells at constant stress.
Further information on data and data acquisition, analysis, and experimental details are given in “Experimental Data from Service-Like Creep-Fatigue Experiments on Grade P92 Steel”, submitted to Data in Brief. Additional analyses of these datasets, as well as experimental findings and discussions are presented in “Creep-Fatigue of P92 in Service-Like Tests with Combined Stress- and Strain-Controlled Dwell Times”, submitted to International Journal of Fatigue.
Due to the increasing scarcity of critical raw materials current high-temperature materials are sought to be replaced by alloys based on more abundant metals. One possibility within the class of intermetallics are iron aluminides, which combine sustainability and cost-efficiency with the prospect of mass savings. Iron aluminides show competitive specific strength up to 700 °C and excellent creep and wet corrosion resistance by small additions of Mo, Ti and B. Nevertheless, a Mo content of above 2 at.% which is needed for optimum corrosion resistance results in enhanced brittleness, especially at room temperature. This is why alloys with these Mo fractions were only mechanically tested under compressive loading so far. Still, testing of static and creep properties under tensile loading is required for reliable component design. Besides high standards for crack-free processing, data acquisition for tensile loads is especially complicated by environmental embrittling effects for iron aluminides. To cope with these challenges, the AiF research project “WAFEAL – Materials applications for iron aluminides” was initiated. The main goal is to collect standardised data on ambient and high-temperature tensile properties and creep properties. Samples with a nominal composition of Fe-26Al-4Mo-0.5Ti-1B [at.%] were manufactured via centrifugal casting in ceramic shell moulds followed by machining. Heat treatment for homogenisation and final polishing were carried out where appropriate. A summary of the achieved tensile and creep properties such as yield and tensile strength, maximum elongation, secondary creep rate and stress exponents will be given. Results will be also discussed regarding the influence of temperature, stress level and microstructure on the damage mechanisms. Furthermore, the effect of different alloy concentrations on the mechanical response at different temperatures will be outlined within a small experimental series.
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.
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.
Substitution of lead oxide in low-melting glasses, e.g., for application in silver metallization pastes, is a current research goal. This work is focused on the investigation of silver diffusion in alkali zinc borate glasses (X2O-ZnO-B2O3, X = Li, Na, K, Rb). In addition, the redox state of silver (Ag) and thus the type of diffusing silver species were studied. For this purpose, a metallic silver coating was applied on glass cubes by means of sputtering. Heat treatment of the samples was performed close to the glass transition temperatures at 470 °C for 2 h under air and nitrogen atmosphere. Coating thickness was 1.8 µm after heat treatment, measured by a white light interferometer.
Silver diffusion profiles were measured by means of secondary neutral mass spectrometry. The silver diffusion coefficients are in the range of ~10-14 cm2/s and indicate no significant differences depending on the type of alkali ions in the glass. Dissolved Ag+-ions and [Agm]n+-clusters in the glasses were differentiated using fluorescence spectroscopy.
Precipitated metallic silver particles in the sodium containing glass were observed by means of transmission electron microcopy. Their mean particle diameter was ~6 nm.
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).
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.