5 Werkstofftechnik
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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.
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.
The mechanical strength of wrought high-strength aluminum alloys is essentially based on precipitation hardening, possibly in combination with prior forming, e. g. by stretching. Important parameters for achieving an optimum combination of hardness, strength, ductility, toughness, and further properties such as corrosion resistance are age-hardening temperature and time.
During thermal (mechanical) treatment, nucleation and growth of precipitates takes place, leading to the desired degree of hardening. In aluminum alloys, precipitation sequences are usually passed through, i. e. a sequence of metastable precipitates is formed before the stable phase can precipitate. The optimum combination of properties is therefore based on a certain (optimum) microstructure, which can, however, change during the use of a component, since the microstructure is not stable. This happens in particular when the operating temperatures are close to the aging temperature and/or the operating times are sufficiently long. An external mechanical load may accelerate the processes. The presentation gives some examples for this.
The presentation shows the results of high cycle fatigue (HCF) tests from the aluminium alloy EN AW-2618A. This study investigates the mean stress influence in the T61 condition and the effect of overaging. For this purpose, axial HCF tests were carried out at room temperature and different stress ratios (R=-1, R=0.1) as well as with overaged conditions (T61+10h@230°C, T61+1000h@230°C). After completion of the tests, the fracture surfaces were examined to study crack initiation.
Thermal spraying enables a fast and propelling way to additively deposit various ceramics as electric insulators, which are used in conditions where polymers are not suitable. Alumina (Al2O3) is among the most employed materials in the coating industry since it exhibits good dielectric properties, high hardness, high melting point while still being cost-effective. Various parameters (e.g. feedstock type, plasma gas mixture, plasma power) significantly influence the resulting coating in terms of microstructure, porosity, crystallinity, and degree of un-or molten particles. As a consequence, these parameters need to be investigated to estimate the impact on the electrical insulating properties of thermally sprayed alumina. This study focuses on the development of a novel electric insulation coating from Al2O3 feedstock powders deposited via atmospheric plasma spray (APS). The microstructure, porosity, and corresponding crystallographic phases have been analyzed with optical microscopy, XRD, and SEM images. To achieve an understanding of the parameters influencing the electrical insulation performance of the manufactured coatings, an in-depth analysis of the fundamental dielectric parameters e.g., DC resistance, breakdown strength, dielectric loss tangent, permittivity is presented.
Multilayer thermoelectric generators are a promising perspective to the conventional π-type generators. Ceramic multilayer technology is well established for production of microelectronics and piezo-stacks. Key features of ceramic multilayer technology are full-automation, cost-effectiveness, and the co-firing of all materials in one single step. This requires similar sintering temperatures of all used materials. The development of multilayer thermoelectric generators is a subject of current research due to the advantages of this technology. One of the challenges is the compatibility of the different materials with respect to the specific design.
The presented study compares three different designs of multilayer generators based on a given set of material properties. Dualleg, unileg and transverse multilayer generators are compared to conventional π-type generators., the designs are evaluated regarding the expected maximum output power and power density using analytical calculations and FEM simulations. Additionally, the complexity of the production process and material requirements are assessed and design optimizations to simplify production are discussed.
Besides the theoretical aspects, unileg multilayer generator prototypes were produced by tape-casting and pressure-assisted sintering. These prototypes are compared to other multilayer generators from literature regarding the power factors of the used material system and the power density. Improvements of the power output by design optimizations are discussed
The interest to additively manufacture Nickel-based superalloys has substantially grown within the past decade both academically and industrially. More specifically, additive manufacturing processes such as laser powder bed fusion (LPBF) offer the ability to produce dense parts within a single manufacturing step. In fact, the exceptional freedom in design associated with the layer-based nature of the processes is of particular interest for the complex shapes typically required in turbine applications. In certain cases, the overall part performance can be achieved by tailoring the microstructure and the crystallographic texture to the specific application. However, these advantages must be paid at a price: the large local temperature gradients associated with the rapid melting and solidification produce parts that inherently contain large residual stress in the as-manufactured state. In addition, the presence of pores in the final part may further affect the in-service part failure. As among Nickel-based alloys Inconel 718 exhibits excellent weldability, this alloy has been widely studied in open research in the domain of LPBF. However, significant microsegregation of the heavier alloying elements such as Niobium and Molybdenum accompanied by dislocation entanglements may preclude the application of conventional heat treatment schedules. Therefore, different post processing heat treatments are required for laser powder bed fused Inconel 718 as compared to conventional variants of the same alloy.
In this study, we investigated two different heat treatment routes for LPBF Inconel 718. In a first routine, the samples were stress relieved and subsequently subjected to hot isostatic pressing (HIP) followed by a solution heat treatment and a two-step age (referred to as FHT). In a second routine, the samples were subjected to a single-step direct age post stress relieving heat treatment (referred to DA). We investigated the consequences of such heat treatment schedules on the microstructure, texture, and mechanical behavior. We show that by applying a DA heat treatment the typical columnar microstructure possessing a crystallographic texture is retained, while an equiaxed untextured microstructure prevails in case of an FHT heat treatment. We further evaluate how these heat treatments affect the mechanical behaviour on the macroscopic and microscopic scale.