Effect of the strain rate on the properties of electrical copper

The effect of the strain rate on the texture and strength characteristics of electrical copper is analyzed using an industrial experiment on low- and high-speed wire drawing. The mechanical properties of the product are determined. The strength of the wire drawn at a high speed is found to be about 20 MPa higher than that of the wire drawn at a low speed. Metallographic analysis shows no differences in the wire structures, and texture analysis reveals differences in the behavior of dominant texture components. The directions of the dominant texture components are found to rotate near the periphery of the workpiece (i.e., at the workpiece surface). The solution of the drawing problem by the finite element method demonstrates an analogous rotation of the principal elongation directions.     

Room-temperature instability of the structure of copper deformed at a cryogenic temperature

The structural changes in cryodeformed copper are studied during long-term (1–2 year) storage at room temperature. The material is found to be unstable: grain growth is detected. The revealed structural instability casts doubt on the use of cryogenic deformation to form a nanocrystalline structure in copper.     

Nanostructure forming in soft magnetic Fe-P-Si-Mn-V and Fe-P-Si-Mn-V-C alloys upon annealing

The crystallization and formation of clusters and nanocrystals in amorphous Fe-P-Si-Mn-V and Fe-P-Si-Mn-V-C alloys during annealing is studied by Mössbauer spectroscopy and physicochemical analysis. The results permit the choice of optimal compositions with soft magnetic properties.     

Ensuring the adhesion and cohesion strength of PVD (TiAl)N and TiN coatings

The fracture of PVD coatings as a result of bulging and cohesion cracking is shown to be controlled by creating a discrete coating surface topography. For longitudinal compression, the discrete region size is calculated using the theory of stability of elastic systems; under tensile stresses, the coating region size is chosen on the basis of the calculated crack step.     

Computational uncertainty analysis in multiresolution materials via stochastic constitutive theory

A stochastic constitutive theory is proposed in this work to propagate microstructure uncertainties in computational multiscale continuum models to bulk multiresolution material behavior. Ubiquitous fine resolution uncertainty sources influencing prediction of material properties based on their structures are categorized in detail, and this research transmits these uncertainties to coarser material resolutions by introducing a stochastic constitutive theory deduced from volume element simulations. To implement the stochastic upscaling process, two advanced uncertainty quantification methods are examined: statistical copula functions and random process polynomial chaos expansion. Both methods confront the mathematical difficulty in randomizing constitutive laws by capturing the marked correlation among constitutive parameters seen in complex materials, thus the results proffer a more accurate probabilistic estimation of constitutive material behavior. The contribution of this work is twofold: uncertainty is propagated from heterogeneous material “structure” to material “property” via the stochastic constitutive theory, and rigorous, data-driven mathematics are formalized to represent complicated dependence structures in multivariate statistical distributions. To the authors’ knowledge, this is the first work in multiresolution mechanics that presents an approach to computationally derive correlation functions from numerical experiments, as opposed, for instance, to assuming one a priori. The method put forth in this research, though quite general, is applied to a mathematical example and plastic, high strength steel alloy for demonstration. Results include stochastic constitutive curve confidence intervals for the material stress–strain response and qualitative comparisons of the two stochastic methods detailed herein.     

Applicability of stress–force–fabric relationship for non-proportional loading

Starting from the micro-structural definition of the stress tensor, Rothenburg and Bathurst (1989) [1] derived a stress–force–fabric relationship for granular materials by approximating the directional distributions of the fabric, more specifically, the contact normal density distribution in this paper and the contact forces with Fourier functions and integrating over directions. This paper aims to assess the validity of the two key assumptions made during their derivation using particle-based numerical simulation in the cases of proportional loading and non-proportional loading. These two assumptions are (i) the 2nd-rank Fourier functions adopted are good enough to approximate the directional distributions of contact normal densities and contact forces and (ii) the principal directions of contact forces and contact normal density are coaxial. Numerical simulations have been carried out to conduct virtual experiments on the behaviour of isotropic specimens to monotonic loading, of isotropic specimens to stress rotation, and of anisotropic specimens to monotonic loading. The first one stands for the case of proportional loading while the latter two are non-proportional loading paths involving rotation of the frame of principal stresses and the frame of fabric, respectively. The directional distributions of contact normal density and contact forces are traced during these three typical loading processes. The simulation results indicate that the 2nd-rank Fourier functions give reasonable approximations, while the coaxial assumption is generally not valid in non-proportional loading. In the case that the principal directions of contact normal density and contact force differ, a more general expression of the stress–force–fabric relationship is required. This research can help to improve our understanding of the stress state and hence shear strength of granular materials based on the particle scale investigation.     

Using hybrid neural networks in a problem of adaptive testing

In the paper, we studied specific features of adaptive testing implementation by means of different methods for the purpose of increasing an objective character of results of such tests and decreasing labor-intensiveness of their creation. We chosen an optimal structure for a hybrid neural network.     

The possibility of using acid-alkali solutions to precipitate rubidium in the form of its hexachlorotelluride

The possibility of isolating rubidium from acid-alcohol solutions in the form of its complex halogenide with tellurium is considered. The solubility of potassium, sodium, and rubidium in solutions of the mixture of hydrochloric acid and ethanol with their various volume ratios at t = 25°C is investigated. The possibility of increasing the degree of isolation of Rb in the form of its hexachlorotellurite (Rb₂TeCl₆) upon going from acidic to acid-alcohol solutions is shown.