Ingenieurwissenschaften und zugeordnete Tätigkeiten
Filtern
Erscheinungsjahr
- 2022 (49) (entfernen)
Dokumenttyp
- Zeitschriftenartikel (49)
Sprache
- Englisch (48)
- Mehrsprachig (1)
Referierte Publikation
- ja (49) (entfernen)
Schlagworte
- Microstructure (4)
- Transmission electron microscopy (4)
- Additive manufacturing (3)
- HIP (3)
- AGIL (2)
- Additive Manufacturing (2)
- Corrosion (2)
- Dislocation (2)
- High entropy alloy (2)
- Interfacial strength (2)
Organisationseinheit der BAM
- 5 Werkstofftechnik (49) (entfernen)
Paper des Monats
- ja (2)
The elastic energy of mixing for multi-component solid solutions is derived by generalizing Eshelby's sphere-in-hole model. By surveying the dependence of the elastic energy on the chemical composition and lattice misfit, we derive a lattice strain coefficient λ*. Studying several high-entropy alloys and superalloys, we propose that most solid solution multi-component alloys are stable when λ*<0.16, generalizing the Hume-Rothery atomic-size rule for binary alloys. We also reveal that the polydispersity index δ, frequently used for describing strain in multi-component alloys, directly represents the elastic energy e with e=qδ², q being an elastic constant. Furthermore, the effects of (i) the number and (ii) the atomic-size distribution of constituting elements on the phase stability of high-entropy alloys were quantified. The present derivations and discussions open for richer considerations of elastic effects in high-entropy alloys, offering immediate support for quantitative assessments of their thermodynamic properties and studying related strengthening mechanisms.
In this paper, columnar cellular growth with kinetic effects including kinetic undercooling and solute trapping in rapid directional solidification of alloys was investigated by using a recent quantitative phase-field model for rapid solidification. Morphological transition and primary spacing selection with and without kinetic effects were numerically investigated. Numerical results show that doublon structure is an intermediate state in the primary spacing adjustment of cellular arrays. It was found that the inclusions of kinetic effects result in the increase of the solute in the solid phase and the solute enrichment in the interdendritic liquid channel. Moreover, predicted results indicate that the growth directions of the cellular arrays in rapid directional solidification with and without kinetic effects are independent of the Péclet number. Therefore, the kinetic effects play important roles in numerical simulations of the growth pattern selection and solute distribution during rapid solidification. Neglecting them will result in the inaccurately predicted results.
Grain boundaries (GBs)’s role in determining the functional and mechanical properties of polycrystalline materials is inscribed in both their structure and chemistry. Upon solute segregation, the structure and composition of a GB can change concurrently. We study the co-evolution of GB’s structure and segregation by enhancing the density-based phase-field model to account for the in-plane structural variations in the GB. Significant mutual coupling is revealed between the GB’s chemical and structural states during Mn segregation in Fe-Mn alloys. We found that the structural degrees of freedom in a GB (the ability of the GB structure to respond to the chemical variation) amplifies Mn segregation transition, even when the GB structure stays unchanged. When the GB structure is not uniform, that is the usual case, the coupling between GB structure and segregation evolution also enables the spinodally formed low- and high-Mn phases (upon segregation transition) to co-exist within the GB region. These findings explain the stabilizing mechanism of pronounced interfacial segregation fluctuations, experimentally evidenced in Fe-Mn GBs, and give new insights on the structural sensitivity of GBs’ segregation phenomena and the mutual chemo-structural interplay.
Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations.
Detailed microstructural characterization of the AlMo0.5NbTa0.5TiZr refractory high entropy superalloy in the as-cast state is reported for first time and compared with the state annealed at 1400 oC for 24 h. The former shows a dendritic structure, with a mixture of A2/B2 phases < 20 nm in both the dendritic and interdendritic regions. A mostly amorphous phase, rich in Al and Zr, is found within the interdendritic region. The annealed state reproduced the combination of A2/B2/Al-Zr-rich phases reported previously. Calculations from two relevant ThermoCalc databases were compared with the experimental results. Equilibrium calculations were compared with results for the annealed alloy, whereas solidification paths calculated using Scheil-Gulliver model were used for comparison with the as-cast alloy. A previously hypothesized spinodal decomposition during cooling as the mechanism responsible for the patterned A2/B2 microstructure is confirmed via the CALPHAD calculations, pointing to its use as an efficient design tool for such alloys. Finally, the comparison between the experimental and computational findings allowed better understanding the solidification path and equilibrium stability of this alloy, giving a base to make better decisions on the field of new refractory superalloy design.
The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples.
In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge.
The interest in repair technologies for polymer composites has increased significantly over the last decades, due to the growing use of these materials in structural applications. In this study, poly (ethylene-co-methacrylic acid) (EMAA) was used as self-healing agent to glass fiber/epoxy composite. Materials with EMAA contents of 2 wt% and 5 wt% were manufactured using Resin Transfer Molding (RTM) and the effects of the healing agent on the properties were investigated using tensile tests and Dynamic Mechanical Analysis (DMA). Results show slight variation of properties, which was more pronounced as the content of EMAA increased. In addition, the healing efficiency was investigated through fatigue tests and the addition of higher content of EMAA increased the number of cycles to failure after the healing activation cycle.
Interpreting high-temperature corrosion induced by mixed-gas atmospheres is challenging due to the different contributions of oxidizing gases. Here, a comprehensive study on the combined oxidation/sulfidation using label molecules is presented. Fe-Cr model alloys with 2 wt% and 9 wt% Cr were isothermally exposed using a volumetric mixture of 0.5%S16O2/27%H218O and 0.5%S16O2/7%H218O at 650 ◦C for 5 h and then characterized by secondary ion mass spectroscopy (SIMS). Additionally, the reactions were followed in-situ utilizing energy dispersive X-ray diffraction. The study showed that both S16O2 and H218O contribute to the oxidation of the alloys but to different extents depending on the Cr-content.
Filigree structures can be manufactured via two-photon-polymerization (2PP) operating in the regime of non-linear light absorption. For the first time it is possible to apply this technique to the powder processing of ceramic structures with a feature size in the range of the critical defect size responsible for brittle fracture and, thus, affecting fracture toughness of high-performance ceramics. In this way, tailoring of advanced properties can be achieved already in the shaping process. Traditionally, 2PP relies on transparent polymerizable resins, which is diametrically opposed to the usually completely opaque ceramic resins and slurries. Here we present a transparent and photocurable suspension of nanoparticles (resin) with very high mass fractions of yttria-stabilized zirconia particles (YSZ). Due to the extremely well dispersed nanoparticles, scattering of light can be effectively suppressed at the process-relevant wavelength of 800 nm. Sintered ceramic structures with a resolution of down to 500 nm were obtained. Even at reduced densities of 1 to 4 g/cm³, the resulting compressive strength with 4,5 GPa is equivalent or even exceeding bulk monolithic yttria stabilized zirconia. A ceramic metamaterial is born, where the mechanical properties of yttria stabilized zirconia are altered by changing geometrical parameters and gives access to a new class of ceramic materials.