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The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies. Reusing these upper-level ontologies and materials testing standards not only makes MTO highly interoperable with other ontologies but also ensures its acceptance and applicability in the industry. MTO represents the mechanical testing entities in the 230 classes and four main parts: i) Mechanical testing experiments entities like tensile, hardness, creep, and fatigue tests as the subclasses of mseo:Experiment, ii) Mechanical testing quantity concepts such as toughness, elongation, and fatigue strength in the appropriate hierarchies of bfo:Disposition and bfo:Quality classes, iii) Mechanical testing artifacts like indenter as the subclasses of cco:Artifact, and iv) mechanical testing data like the stress-strain, S-N, or creep curves as the subclasses of cco:InformationContentEntity. MTO is publicly available via the KupferDigital GitLab repository.
Nickel-based alloys have been widely used for gas turbine blades owing to their excellent mechanical properties and corrosion resistance at high temperatures. The operating temperatures of modern gas turbines have been increased in pursuit of increased thermal efficiency. Turbine blades are exposed to these high temperatures combined with mechanical stresses, resulting in material damage through creep, fatigue, and other mechanisms. These turbine blades must be regularly inspected and replaced as needed, to prevent the loss of efficiency, breakdown, and catastrophic failure. Repair of the damaged turbine blades is often a more practical and cost-effective option than replacement, as replacement is associated with high costs and loss of material resources. To this end, state-of-the-art repair technologies including different additive manufacturing and brazing processes are considered to ensure efficient repair and optimum properties of repaired components.
In any repaired part, materials property-mismatches and/or inner defects may facilitate the crack initiation and propagation and thus reduce the number of load cycles to failure. Therefore, a fundamental understanding of the fatigue crack growth and fracture mechanisms in joining zones is required to enable the prediction of the remaining life of repaired components and to further improve and adapt the repair technologies.
Fatigue crack growth experiments have been conducted on SEN (Single Edge Notch) specimens joined via brazing, and pre-sintered Preform (PSP) and multi-materials (casted/printed) specimens layered via additive manufacturing (AM). The experiments were performed at 950 °C and various stress ratios. The crack growth was measured using DCPD (Direct Current Potential Drop) method. The stress intensity factors for joined SEN specimens were calculated using the finite element method and then used to derive the fatigue crack growth curves. Metallographic and fractographic analyses were conducted to get insight into the fracture mechanism.
Results show that the experimental technique for fatigue crack growth was successfully adapted and applied for testing joined specimens. Furthermore, the initial tests indicate that the investigated braze filler material provides a lower resistance to crack growth, and bonding defects cause a crack to deviate to the interface of the base material and joining zone. In AM-sandwich specimens, the crack growth rates are significantly reduced when the crack reaches the interface of printed material and casted material. The obtained crack growth data can be used to calibrate a crack growth model, which will further be utilized to predict the remaining life of repaired components.
Experimentally informed multiscale creep modelling of additive manufactured Ni-based superalloys
(2023)
Excellent creep resistance at elevated temperatures, i.e. T / T_m> 0.5, due to γ-γ’ microstructure is one of the main properties of nickel-based superalloys. Due to its great importance for industrial applications, a remarkable amount of research has been devoted to understanding the underlying deformation mechanism in a wide spectrum of temperature and loading conditions. Additive manufactured (AM) nickel-based superalloys while being governed by similar γ-γ’ microstructure, exhibit AM-process specific microstructural characteristics, such as columnar grains, strong crystallographic texture (typically <001> fiber texture parallel to build direction) and compositional inhomogeneity, which in turn leads to anisotropic creep response in both stationary and tertiary phases.
Despite the deep insights achieved recently on the correlation between process parameters and the resulting microstructure, the anisotropic creep behavior and corresponding deformation mechanism of these materials are insufficiently understood so far. One reason for this is the lack of capable material models that can link the microstructure to the mechanical behavior. To overcome this challenge, a multiscale microstructure-based approach has been applied by coupling crystal plasticity (CP) and polycrystal model which enables the inclusion of different deformation mechanisms and microstructural characteristics such as crystallographic texture and grain morphology. The method has been applied to experimental data for AM-manufactured INCONEL-738LC (IN738). The effect of different slip systems, texture, and morphology on creep anisotropy at 850°C has been investigated. Results suggest a strong correlation between superlattice extrinsic stacking fault (SESF) and microtwinning and observed creep anisotropy.
1. Introduction
A normally unwanted process that can arise when converging an electron beam onto, e.g. microparticles, has been called "damage induced by electric field" (DIEF) [1]. By DIEF, the convergent electron beam (CEB) imparts a high amount of energy to the microparticle locally and strongly interacts with its atoms. At a specific current density J, which can be controlled by the convergence angle α, the irradiated material begins to transform. The phenomenon of expelling nanomaterial from microparticles under the influence of a convergent electron beam (CB) in a transmission electron microscope (TEM) has been largely studied [2]. Several types of nanoparticles (NPs) have been observed for different metallic materials and metal oxides after specific CB protocols (P) in the TEM. Thus, DIEF can be used as a promising synthesis method controlled changes of micrometric material to create new nanometric material compositions and morphologies.
While these reactions have been observed in situ at the high acceleration voltages associated with TEM, it remains unclear whether the SEM can also be used to fabricate NPs via DIEF. In contrast to TEM there is no possibility to statically convert the electron beam to a range of α to reach the needed J as in TEM. Instead, the scanning parameters and the magnification can be manipulated so as to find an integrated J. Considering that the scanning electron microscope (SEM) is easier to use, more accessible and cheaper than a TEM, here we explore the possibility to transfer the concepts of DIEF known to operate in the TEM for in situ NP generation SEM.
2. Objectives
The main goal is to determine whether DIEF can be translated to the SEM perform to controlled in situ fabrication of nanoparticles from microparticles, using gold microparticles on amorphous SiO substrate as precursors. We determine what experimental parameters must be taken into account to create SEM-based CBPs for NP creation in the SEM with these materials.
3. Materials & methods
Gold microparticles with diameter of around 1 to 3 µm were deposited on electron transparent amorphous SiO/SiO2 substrate. Using a convergent electron beam protocol (CBP) in a scanning electron microscope (SEM) at an acceleration voltage of 30 kV, the gold microparticles were irradiated until a production of NPs takes place as shown in figure 1. The beam current varied between 16 and 23 nA.
4. Results
Depending on the CBP parameters, either only Au NPs or a mixture of Au and Si NPs are produced. The particle size ranges from a few nm up to 100 nm, and it depends on the distance of the NP to the initial position of the microparticle. Further beam parameters such as the dwell time, the effective irradiated volume and particle size determine whether NPs are produced or if the microparticles only are expelled from the substrate without reacting.
5. Conclusion
The SEM can be used as an instrument for synthesizing nanomaterials via DIEF. Different CBP protocols can be applied for obtaining either gold nanoparticles or silicon + gold nanoparticles
The phenomenon of expelling nanomaterial from microparticles of different materials, such as Au, WO3 or B2O3 under the influence of a convergent electron beam (CB) of a transmission electron microscope (TEM) was reviewed by Ignacio Gonzalez-Martinez [1]. Converging the e-beam in a TEM means that a high amount of energy enters the microparticle at a very local place and interact with the matter. Obviously, during the convergent beam protocol, no imaging with the electron beam is possible, but at the end, nanoparticles with different appearances lie down next to the microparticle while its size is reduced.
Hence, there is a blind spot in the observation, which we want to fill, as we want to help clarify the nature of the expelling phenomenon. One hypothesis that explains the phenomenon is the so-called damage (of the microparticle) induced by an electric field (DIEF). Within this theory, the material is ionized and expelled in form of ionic waves. Our aim is therefore to fabricate specimens with artificial microlandscapes, as schematically exemplified in figure 1a), using the focused ion beam (FIB) and micromanipulators, as experimental setups to follow the paths of the expelled material.
As a first step towards the fabrication of such specimen, we make experimental feasibility studies for each fabrication method, FIB structuring with Ga+ ion beam and micromanipulated microparticle deposition. Bridges (gray regions in Fig. 1) are created by milling a commercially available electron transparent membrane (silicon oxide or carbon) of a Cu-TEM grid. Platinum or carbon walls (blue features in Fig. 1) are built to stand on those bridges. Microparticles (yellow sphere in Fig. 1) of gold or other material are deposited in the center of the bridges.
Figure 2a) shows four square holes (black area) and between them the residual silicon oxide membrane bridges (dark grey). On top of the bridges, walls (light grey) are deposited. The width of the bridges is different, the walls overlap the holes as well as the distance between the walls is very small, so these and other parameters need to be optimized. Figure 2b) shows a square hole (black) with bridges (white) on the right side on top of a carbon membrane (grey). There are still some obstacles which needs to be eliminated. For instance, the deposition process of the walls is not reliable as visible at the wall on top where a hole arises instead of a wall.
These studies are still in progress and the results are further discussed in terms of the applicability for the DIEF experiment in the TEM.
Recent advances in Deep Learning (DL) have significantly impacted the field of materials modelling. DL tools have been recently considered as promising tools to address the complex relationships among processing, microstructure and property of materials. The thermal stability of polycrystalline materials is a highly interesting and complex problem that could be addressed using DL techniques. The Multi-Phase-Field (MPF) method has emerged as a powerful tool for addressing grain growth phenomena from multiple perspectives. Unlike sharp-interface based methods, the MPF approach bypasses the need for detailed information on individual grains. In this work, we use DL to address issues related to MPF simulations of grain growth including numerical efficiency, computing speed, and resource consumption. This presents specific challenges for high-performance computing (HPC) due to the large datasets and complex computations required by both MPF and DL methods. We study various 3D microstructure settings with the goal of accelerating the simulation process while exploring different physical effects. In particular, the impact of grain boundary and triple junction energies on grain growth are to be investigated. The results will be presented in terms of the evolving size and shape distribution of the grains.
Many metallic materials gain better mechanical properties through controlled heat treatments. For example, in age-hardenable aluminium alloys, the strengthening mechanism is based on the controlled formation of nanometre-sized precipitates, which represent obstacles to dislocation movement and consequently increase the strength. Precise tuning of the material microstructure is thus crucial for optimal mechanical behaviour under service condition of a component. Therefore, analysis of the microstructure, especially the precipitates, is essential to determine the optimum parameters for the interplay of material and heat treatment. Transmission electron microscopy (TEM) is utilized to identify precipitate types and orientations in the first step. Dark-field imaging (DF-TEM) is often used to image the precipitates and thereafter quantify their relevant dimensions. Often, these evaluations are still performed by manual image analysis, which is very time-consuming and to some extent also poses reproducibility problems.
Our work aims at a semantic representation of an automatable digital approach for this material specific characterization method under adaption of FAIR data practices. Based on DF-TEM images of different precipitation states of a wrought aluminium alloy, the modularizable, digital workflow of quantitative analysis of precipitate dimensions is described. The integration of this workflow into a data pipeline concept will also be discussed. Using ontologies, the raw image data, their respective contextual information, and the resulting output data of the quantitative image analysis can be linked in a triplestore. Publishing the digital workflow and the ontologies will ensure data reproducibility. In addition, the semantic structure enables data sharing and reuse for other applications and purposes, demonstrating interoperability.
We report about a joint project aiming at the digitalization of a lab course in materials testing. The undergraduate students were asked to prepare samples of a precipitation hardened aluminum alloy and characterize them using hardness and tensile tests. In a first step, we developed the frames for the digital labor notebook using eLabFTW. The primary data and the relevant metadata of each run were saved in a central database and made available for analysis and report issues. The whole set of results produced in a course was made available in the database. This database can be improved and serve as an open repository for data on this specific alloy.
The logical frame for the joint project was provided by the PMD Core Ontology (PMDco), a mid-level ontology that enables the representation and description of processes and process chains in an MSE-specific manner, ensuring full traceability of generated data. For the digitalization of this lab course, the tensile test ontology (TTO) was applied which is designed as a module of the PMDco using strongly related semantic concepts.
Many microstructural features exhibit non-trivial geometries, which can only be derived to a limited extent from two-dimensional images. E.g., graphite arrangements in lamellar gray cast iron have complex geometries, and the same is true for additively manufactured materials and three-dimensional conductive path structures. Some can be visualized using tomographic methods, but some cannot be due to weak contrast and/or lack of resolution when analyzing macroscopic objects. Classic metallography can help but must be expanded to the third dimension. The method of reconstructing three-dimensional structures from serial metallographic sections surely is not new. However, the effort required to manually assemble many individual sections into image stacks is very high and stands in the way of frequent application. For this reason, an automated, robot-supported 3D metallography system is being developed at BAM, which carries out the steps of repeated preparation and image acquisition on polished specimen.
Preparation includes grinding, polishing and optionally etching of the polished surface. Image acquisition comprises autofocused light microscopic imaging at several magnification levels. The image stacks obtained are then pre-processed, segmented, and converted into 3D models, which in the result appear like microtomographic models, but with high resolution at large volume. Contrasting by classical chemical etching reveals structures that cannot be resolved using tomographic methods. The integration of further imaging and measuring methods into this system is underway. Some examples will be discussed in the presentation.
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 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.