Texture selection mechanisms in uniaxially extruded magnesium alloys

Extrusion textures in magnesium alloys are of high interest due to their influence on yield asymmetry. This data supports work describing three mechanisms of texture selection that may play a role during extrusion. These mechanisms involve localized differences in deformation at the grain level, the change in local environment experienced by grain boundary bulges and shear banding. The work employs visco-plastic crystal plasticity and electron backscattering diffraction.

Variation of yield loci in finite element analysis by considering texture evolution for AA5042 aluminum sheets

 Yield function has various material parameters that describe how materials respond plastically in given conditions. However, a significant number of mechanical tests are required to identify the many material parameters for yield function. In this study, an effective method using crystal plasticity through a virtual experiment is introduced to develop the anisotropic yield function for AA5042. The crystal plasticity approach was used to predict the anisotropic response of the material in order to consider a number of stress or strain modes that would not otherwise be evident through mechanical testing. A rate-independent crystal plasticity model based on a smooth single crystal yield surface, which removes the innate ambiguity problem within the rate-independent model and Taylor model for polycrystalline deformation behavior were employed to predict the material’s response in the balanced biaxial stress, pure shear, and plane strain states to identify the parameters for the anisotropic yield function of AA5042.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Microstructure and texture evolution during tensile deformation of symmetric/asymmetric-rolled low carbon microalloyed steel

The deformation and fracture mechanisms of a low carbon microalloyed steel processed by asymmetric rolling (AsR) and symmetric rolling (SR) were compared by microstructural and texture evolutions during uniaxial tensile deformation. A realistic microstructure-based micromechanical modeling was involved as well. AsR provides more effective grain refinement and beneficial shear textures, leading to higher ductility and extraordinary strain hardening with improved yield and ultimate tensile stresses as well as promoting the occurrence of ductile fracture. This was verified and further explained by means of the different fracture modes during quasi-static uniaxial deformation, the preferred void nucleation sites and crack propagation behavior, and the change in the dislocation density based on the kernel average misorientation (KAM) distribution. The equivalent strain/stress partitioning during tensile deformation of AsR and SR specimens was modeled based on a two-dimensional (2D) representative volume element (RVE) approach. The trend of strain/stress partitioning in the ferrite matrix agrees well with the experimental results.

Analysis of texture and R value variations in asymmetric rolling of if steel

Asymmetric rolling (ASR) is a potential process to reach better grain refinement than in conventional rolling, thus, can lead to better mechanical properties. It is not known, however, how the introduction of a shear component will change the ideal orientations of the textures, and consequently, the evolution of plastic anisotropy. To understand the effect of the added shear on texture evolution in ASR, a stability analysis is carried out in orientation space and the variations in the position and strength of the ideal orientations are analysed as a function of the shear component. Then, modelling of R values is presented for various cases. On that basis, it is shown that there is an upper limit for the shear component in asymmetric rolling that still retains the 〈1 1 1〉 ND fibre (ND: direction normal to the sheet) which is good for formability. It is also found that better persistence of the ND fibre can be obtained by cyclically alternating the shear component. The theoretical results are well supported by comparison to experimental evidences. © 2011 Elsevier B.V. All rights reserved.

Texture evolution and grain refinement of ultrafine-grained copper during micro-extrusion

Samples of oxygen-free high conductivity (OFHC) coarse-grained (CG) and ultrafine-grained (UFG) copper were micro-extruded to an equivalent strain of 2.8 in one pass at room temperature. Samples of the OFHC copper were annealed at 650C for 2 h to produce CG copper. Some samples were subsequently processed by equal channel angular pressing of eight passes, route Bc, at room temperature to produce the UFG material. Crystallographic texture and misorientation distributions were obtained locally from EBSD mappings at different radial positions after micro-extrusion. To model the strain path during micro-extrusion, the analytic flow line model of Altan etal. [J Mater. Process. Tech. 33 (1992) p.263] was used and also validated by finite element calculations. Modelling was carried out using the viscoplastic self-consistent (VPSC) model and a recently developed grain refinement model. The results showed large texture variations along the cross-section of the extruded sample for both UFG and CG copper. These cyclic drawing textures in UFG copper were simulated in good agreement with experiments using the presented modelling framework.