Growth and texture of Spark Plasma Sintered Al2O3 ceramics: a combined analysis of X-rays and Electron Back Scatter Diffraction

Textured alumina ceramics were obtained by Spark Plasma Sintering (SPS) of undoped commercial a-Al2O3 powders. Various parameters (density, grain growth, grain size distribution) of the alumina ceramics, sintered at two typical temperatures 1400{\deg}C and 1700{\deg}C, are investigated. Quantitative textural and structural analysis, carried out using a combination of Electron Back Scattering Diffraction (EBSD) and X-ray diffraction (XRD), are represented in the form of mapping, and pole figures. The mechanical properties of these textured alumina ceramics include high elastic modulus and hardness value with high anisotropic nature, opening the door for a large range of applicationsComment: 16 pages, 6 figures, submitted to J. Appl. Phy

A Phase Field Model for Continuous Clustering on Vector Fields

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model, which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns-the actual clustering-during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters as a function of the underlying flow field. In addition, the model is expanded, involving elastic effects. In the early stages of the evolution shear layer type representation of the flow field can thereby be generated, whereas, for later stages, the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop-shaped appearance. Here, we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.

Spark plasma sintering as an effective texturing tool for reprocessing recycled HDDR Nd-Fe-B magnets with lossless coercivity

The low-pressure hot-deformation methodology was applied to reprocess the nanocrystalline hydrogenation–disproportionation–desorption–recombination (HDDR) Nd-Fe-B powders from end-of-life (EOL) permanent magnets’ waste to determine the mechanism of texture development and the resultant improvement in remanence (and BH$_{max}$) in the recycled material. Both the hot-pressed and hot-deformed magnets produced via spark plasma sintering (SPS) were compared in terms of their magnetic properties with respect to forging pressures. Also, a comparison was established with the microstructure to cite the effectiveness of texture development at low deformation rates and pressures which is pivotal for retaining high coercivity. The hot-pressed magnets maintain the high coercivity (better than 100%) of the original recycled powder due to the control of SPS conditions. The hot deformation pressure was varied from 100–150 MPa at 750 °C processing temperature to identify the optimal texture development in the sintered HDDR Nd-Fe-B magnets. The effect of post-hot-deformation thermal treatment was also investigated, which helped in boosting the overall magnetic properties and better than the recycled feedstock. This low-pressure hot deformation process improved the remanence of the hot-pressed magnet by 11% over the starting recycled powder. The M$_r$/M$_S$ ratio which was 0.5 for the hot-pressed magnets increased to 0.64 for the magnets hot-deformed at 150 MPa. Also, a 55% reduction in height of the sample was achieved with the c-axis texture, indicating approximately 23% higher remanence over the isotropic hot-pressed magnets. After hot deformation, the intrinsic coercivity (H$_{Ci}$) of 960 kA/m and the remanence (B$_r$) value of 1.01 T at 150 MPa is indicative that the controlled SPS reprocessing technique can prevent microstructure related losses in the magnetic properties of the recycled materials. This route also suggests that the scrap Nd-Fe-B magnets can be treated with recoverable magnetic properties subsequently via HDDR technique and controlled hot deformation with a follow-up annealing

Differential Filtering and Detexturing

Extracting valuable information from 2D or 3D visual data plays an important role in image and geometry processing. Surfaces obtained through a scanning process or other reconstruction algorithms are inevitably noisy due to error in the scanning process and resampling of the data at various processing steps. These surfaces need to be denoised both for aesthetic reasons and for further geometry processing. Similarly, extracting or removing texture patterns from 2D or 3D data is challenging due to the complication of its statistical features. In this dissertation, I describe how to remove surface noise and image texture patterns. In particular, I focus on denoising triangulated models based on L0 minimization, in which a very important discrete differential operator for arbitrary triangle meshes has been developed. Compared to other anisotropic denoising algorithms, our method is more robust than other anisotropic denoising algorithms, and produces good results even in the presence of high noise. I also introduce how to use bilateral filter appropriately on image texture removal by modifying its range image. While current existing methods either fail to remove the textures completely or over blur main structures, our method delivers best-in-class image detexturing performance

Image Segmentation using PDE, Variational, Morphological and Probabilistic Methods

The research in this dissertation has focused upon image segmentation and its related areas, using the techniques of partial differential equations, variational methods, mathematical morphological methods and probabilistic methods. An integrated segmentation method using both curve evolution and anisotropic diffusion is presented that utilizes both gradient and region information in images. A bottom-up image segmentation method is proposed to minimize the Mumford-Shah functional. Preferential image segmentation methods are presented that are based on the tree of shapes in mathematical morphologies and the Kullback-Leibler distance in information theory. A thorough evaluation of the morphological preferential image segmentation method is provided, and a web interface is described. A probabilistic model is presented that is based on particle filters for image segmentation. These methods may be incorporated as components of an integrated image processed system. The system utilizes Internet Protocol (IP) cameras for data acquisition. It utilizes image databases to provide prior information and store image processing results. Image preprocessing, image segmentation and object recognition are integrated in one stage in the system, using various methods developed in several areas. Interactions between data acquisition, integrated image processing and image databases are handled smoothly. A framework of the integrated system is implemented using Perl, C++, MySQL and CGI. The integrated system works for various applications such as video tracking, medical image processing and facial image processing. Experimental results on this applications are provided in the dissertation. Efficient computations such as multi-scale computing and parallel computing using graphic processors are also presented

Colour, texture, and motion in level set based segmentation and tracking

This paper introduces an approach for the extraction and combination of different cues in a level set based image segmentation framework. Apart from the image grey value or colour, we suggest to add its spatial and temporal variations, which may provide important further characteristics. It often turns out that the combination of colour, texture, and motion permits to distinguish object regions that cannot be separated by one cue alone. We propose a two-step approach. In the first stage, the input features are extracted and enhanced by applying coupled nonlinear diffusion. This ensures coherence between the channels and deals with outliers. We use a nonlinear diffusion technique, closely related to total variation flow, but being strictly edge enhancing. The resulting features are then employed for a vector-valued front propagation based on level sets and statistical region models that approximate the distributions of each feature. The application of this approach to two-phase segmentation is followed by an extension to the tracking of multiple objects in image sequences

Extension to pv optics to include front electrode design in solar cells

Proper optical designing of solar cells and modules is of paramount importance towards achieving high photovoltaic conversion efficiencies. Modeling softwares such as PV OPTICS, BIRANDY and SUNRAYS have been created to aid such optical designing of cells and modules; but none of these modeling packages take the front metal electrode architecture of a solar cell into account. A new model, has been developed to include the front metal electrode architecture to finished solar cells for optical calculations. This has been implemented in C++ in order to add a new module to PV OPTICS (NREL’s photovoltaic modeling tool) to include front metallization patterns for optical design and simulation of solar cells. This new addition also calculates the contribution of light that diffuses out of the illuminated (non-metallized) regions to the solar cell current. It also determines the optical loss caused by the absorption in the front metal and separates metallic losses due to front and back contacts. This added capability also performs the following functions: calculates the total current that can be generated in a solar cell due to optical absorption in each region, including the region beneath the front metal electrodes for the radiation spectrum of AM 1.5, calculates various losses in the solar cell due to front electrode shading, metal absorption, and reflectance, makes a plot of how light is absorbed in the metal as well as silicon under the shaded region in the solar cell. Although Finite Difference Time Domain (FDTD) is the numerical technique of choice to solve Maxwell’s equations for a propagating electromagnetic wave, it is both time consuming and very demanding on the computer processors. Furthermore, for complicated geometric structures, FDTD poses various limitations. Hence, ray tracing has been chosen as the means of implementing this new model. This new software has been used to carry out a detailed investigation on the effect of various parameters of the front electrode architecture on the performance of alkaline anisotropically texture etched (100) oriented single crystal silicon solar cells. These parameters include: the thickness of the silicon absorber layer, the texture height, width of the front metal fingers, height of the front metal fingers, and the effect of encapsulation of a solar cell in a module. The results show that the front metal architecture used in commercial silicon solar cells has minimal effect on its performance. A decline in the total current derived from the cell encapsulated in a module is also observed. This has helped to narrow down the design variables of commercial silicon solar cells with the standard front electrode grid of fingers and busbars to only the electrical transport