Calculation of the electromigration wind force in Al alloys

The electromigration wind force in various Al alloys is calculated using a Green’s-function method for the calculation of the electronic structure. The influence of the environment of the jumping atoms is studied in detail in the Al-Cu alloy. Alloys of Al with (Formula presented) and (Formula presented) alloying elements are studied systematically in order to investigate the relation between the electronic states of the alloying atom and the wind force. The study also includes several other alloys, which have been used in the past in attempts to increase electromigration lifetime. It is shown that the wind force on an Al host atom can be changed considerably by the presence of an alloying atom at particular positions near the jump path. This could be an additional contribution to the well-known decelerating effect of some alloying elements on electromigration in Al

Mechanical properties of tungsten alloys with Y2O3 and titanium additions

In this research the mechanical behaviour of pure tungsten (W) and its alloys (2 wt.% Ti–0.47 wt.% Y2O3 and 4 wt.% Ti–0.5 wt.% Y2O3) is compared. These tungsten alloys, have been obtained by powder metallurgy. The yield strength, fracture toughness and elastic modulus have been studied in the temperature interval of 25 °C to 1000 °C. The results have shown that the addition of Ti substantially improves the bending strength and toughness of W, but it also dramatically increases the DBTT. On the other hand, the addition of 0.5% Y2O3, is enough to improve noticeably the oxidation behaviour at the higher temperatures. The grain size, fractography and microstructure are studied in these materials. Titanium is a good grain growth inhibitor and effective precursor of liquid phase in HIP. The simultaneous presence of Y2O3 and Ti permits to obtain materials with low pores presenc

Understanding of the phase transformation from fullerite to amorphous carbon at the microscopic level

We have studied the shock-induced phase transition from fullerite to a dense amorphous carbon phase by tight-binding molecular dynamics. For increasing hydrostatic pressures P, the C60-cages are found to polymerise at P<10 GPa, to break at P~40 GPa and to slowly collapse further at P>60 GPa. By contrast, in the presence of additional shear stresses, the cages are destroyed at much lower pressures (P<30 GPa). We explain this fact in terms of a continuum model, the snap-through instability of a spherical shell. Surprisingly, the relaxed high-density structures display no intermediate-range order.Comment: 5 pages, 3 figure

Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

Background Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations. Objective The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies. Methods To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization. Results A workflow with a high degree of automation is established, that links large distortion-corrected microstructure data with damage localization and evolution kinetics. The workflow enables cycling up to the VHCF regime in comparatively short time spans, while maintaining unprecedented time resolution of damage evolution. Resulting data sets capture the interaction of damage with microstructural features and hold the potential to unravel a mechanistic understanding. Conclusions The proposed workflow lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials

Early deformation mechanisms in the shear affected region underneath a copper sliding contact

Dislocation mediated plastic deformation decisively influences the friction coefficient and the microstructural changes at many metal sliding interfaces during tribological loading. This work explores the initiation of a tribologically induced microstructure in the vicinity of a copper twin boundary. Two distinct horizontal dislocation traces lines (DTL) are observed in their interaction with the twin boundary beneath the sliding interface. DTL formation seems unaffected by the presence of the twin boundary but the twin boundary acts as an indicator of the occurring deformation mechanisms. Three concurrent elementary processes can be identified: simple shear of the subsurface area in sliding direction, localized shear at the primary DTL and crystal rotation in the layers above and between the DTLs around axes parallel to the transverse direction. Crystal orientation analysis demonstrates a strong compatibility of these proposed processes. Quantitatively separating these different deformation mechanisms is crucial for future predictive modeling of tribological contacts

A deep learning approach for complex microstructure inference

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning’s seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30–50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology

Nonlinear lattice model of viscoelastic Mode III fracture

We study the effect of general nonlinear force laws in viscoelastic lattice models of fracture, focusing on the existence and stability of steady-state Mode III cracks. We show that the hysteretic behavior at small driving is very sensitive to the smoothness of the force law. At large driving, we find a Hopf bifurcation to a straight crack whose velocity is periodic in time. The frequency of the unstable bifurcating mode depends on the smoothness of the potential, but is very close to an exact period-doubling instability. Slightly above the onset of the instability, the system settles into a exactly period-doubled state, presumably connected to the aforementioned bifurcation structure. We explicitly solve for this new state and map out its velocity-driving relation

Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models

Crack initiation governs high cycle fatigue life and is sensitive to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models

Micromechanical fatigue experiments for validation of microstructure-sensitive fatigue simulation models

Crack initiation governs high cycle fatigue life and is sensitive to microstructural details. While corresponding microstructure-sensitive models are available, their validation is difficult. We propose a validation framework where a fatigue test is mimicked in a sub-modeling simulation by embedding the measured microstructure into the specimen geometry and adopting an approximation of the experimental boundary conditions. Exemplary, a phenomenological crystal plasticity model was applied to predict deformation in ferritic steel (EN1.4003). Hotspots in commonly used fatigue indicator parameter maps are compared with damage segmented from micrographs. Along with the data, the framework is published for benchmarking future micromechanical fatigue models