The textural properties and microstructure of konjac glucomannan - tungsten gels induced by DC electric fields

Konjac glucomannan - tungsten (KGM-T) gels were successfully prepared under DC electric fields, in the presence of sodium tungstate. The textural properties and microstructure of the gels were investigated by Texture Analyzer, Rheometer and SEM. Based on the response surface methodology (RSM) results, the optimum conditions for KGM-T gel springiness is 0.32% sodium tungstate concentration, 0.54% KGM concentration, 24.66V voltage and 12.37min treatment time. Under these conditions, the maximum springiness value of KGM-T gel is 1.21mm. Steady flow measurement indicated that KGM-T gel showed characteristic non-Newtonian pseudoplastic behaviour, with low flow behaviour indexes in the shear thinning region. SEM demonstrated the porosity of the freeze-dried samples. These findings may pave the way to use DC electric fields for the design and development of KGM gels and to apply KGM gels for practical applications.

Influence of deformation conditions and texture on the high temperature flow stress of magnesium AZ31

The evolution of hot working flow stress with strain is examined in torsion, uniaxial compression and channel die compression. The flow stress was found to be strongly dependent on texture and deformation mode. At low strains this dependency accounted for a difference in flow stress of up to a factor of two. At higher strains the influence of texture and deformation mode was less marked. The stresses corresponding to an equivalent strain of 0.5 were modelled using a power law expression with an activation energy of 147 kJ/mol and a strain rate exponent of 0.15. The influence of texture and deformation mode on flow stress is rationalised in terms of the influence of prismatic slip, twinning and dynamic recrystallisation on deformation stress and structure.

Accounting narrative evaluation: using texture as a measure of quality

This paper applies a texture-based approach to evaluating accounting narratives. We employ the texture index created by Sydserff and Weetman (1999) as it is purported to evaluate narratives more accurately than previous methods. Not enough emphasis has been placed on the utilisation of linguistic tools in the accounting context. We see that a rigorous test serves the accountability function and may contribute positively to investor protection and confidence. We support the contention that improving narrative evaluation will help increase the quality of accounting narratives as they are presented in corporate annual reports. Quality for purpose of this paper is defined as a function of readability and understandability. These characteristics are considered essential for informed investment decision-making. We focus on the usefulness of the texture index in determining an appropriate measure of both readability and understandability by applying the texture index to a sample of corporate annual reports. Our results show conditional support for the use of the texture index as a viable alternative to readability tests.

The influence of solute carbon in cold-rolled steels on shear band formation and recrystallization texture

Two experiments were conducted to clarify the roles of grain size, solute carbon and strain in determining the recrystallization textures of cold-rolled and annealed steels. In the first experiment, samples of coarse-grained low-carbon (LC) and interstitial-free (IF) steels were cold-rolled to a 75% reduction in thickness. One sample from each steel was polished and cold-rolled an additional 5%, while the remaining samples were annealed for various times at 650°C. In the second experiment, three samples from a commercial LC steel sheet were rolled 70% at 300°C. Two of the samples were given a further rolling reduction of 5% of the original thickness, with one of the samples being given this additional reduction at 300°C and the other at room temperature. Goss recrystallization textures are strengthened by coarse initial grain sizes, the presence of solute carbon and rolling at a temperature where dynamic strain ageing occurs, but are weakened by additional rolling beyond a reduction of 70%, especially when this extra rolling is conducted at a temperature where dynamic strain ageing does not occur. Characterization of key features of the deformed and recrystallized steels using optical microscopy, scanning electron microscopy (SEM) and electron back-scatter diffraction (EBSD) supports a rationale for these effects based on the repeated activation and deactivation of shear bands and the influence of solute carbon and dynamic strain ageing on the operating life of the bands and the accumulation of strain within them.

Texture, twinning and uniform elongation of wrought magnesium

A small number of crystal plasticity simulations and tensile tests are carried out with the aim of demonstrating that control of twinning can improve the uniform elongation of magnesium based alloys, It is suggested that this can be accomplished through texture manipulation because texture influences both the fraction of grains that undergo twinning and the strain required for the twinning reaction to go to completion.

The application of ant colony systems to image texture classification

This paper presents a novel ant system based optimisation method which integrates genetic algorithms and simplex algorithms. This method is able to not only speed up the search process for solutions, but also improve the quality of the solutions. In this paper, the proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on aerial images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

Genetic learning based texture surface inspection

This paper presents a novel approach of visual inspection for texture surface defects. It is based on the measure of texture energy acquired by a kind if high performance 2D detection mask, which is learned by genetic algorithms. Experimental results of texture defect inspection on textile images are presented to illustrate the merit and feasibility of the proposed method.

Hybrid ant colony algorithm for texture classification

We present a novel ant colony algorithm integrating genetic algorithms and simplex algorithms. This method is able to not only speed up searching process for optimal solutions, but also improve the quality of the solutions. The proposed method is applied to set up a learning model for the "tuned" mask, which is used for texture classification. Experimental results on real world images and comparisons with genetic algorithms and genetic simplex algorithms are presented to illustrate the merit and feasibility of the proposed method.

The reduction subset based on rough sets applied to texture classification

The rough set is a new mathematical approach to imprecision, vagueness and uncertainty. The concept of reduction of the decision table based on the rough sets is very useful for feature selection. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The methods applied include continuous data discretization based on Fuzzy c-means and, and rough set method for feature selection and reduction. The trees extractions in the aerial images were applied. The experiments show that the methods presented in this paper are practical and effective.

Analysis of extracted fingerprint texture features

This paper describes an approach for electronic feature extraction from fingerprint images for identification purposes. Statistical analysis of the features extracted from the fingerprint provides the advantage of real-time analysis of the fingerprint information, providing a method to remove unwanted information prior to the matching stage required in electronic fingerprint verification. This method can also detect of noise within the fingerprint image and location of critical data in the fingerprint. In order to demonstrate the effectiveness of this work, experiments have been developed and conducted with the results and analysis provided at the end of this paper for discussion.

Objective grading of fabric pilling with wavelet texture analysis

A new objective fabric pilling grading method based on wavelet texture analysis was developed. The new method created a complex texture feature vector based on the wavelet detail coefficients from all decomposition levels and horizontal, vertical and diagonal orientations, permitting a much richer and more complete representation of pilling texture in the image to be used as a basis for classification. Standard multi-factor classification techniques of principal components analysis and discriminant analysis were then used to classify the pilling samples into five pilling degrees. The preliminary investigation of the method was performed using standard pilling image sets of knitted, woven and non-woven fabrics. The results showed that this method could successfully evaluate the pilling intensity of knitted, woven and non-woven fabrics by selecting the suitable wavelet and associated analysis scale.

Modeling the flow behavior, recrystallization, and crystallographic texture in hot-deformed Fe-30 Wt Pct Ni Austenite

The present work describes a hybrid modeling approach developed for predicting the flow behavior, recrystallization characteristics, and crystallographic texture evolution in a Fe-30 wt pct Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 °C and 1050 °C and strain rates between 0.1 and 10 s⁻¹. The evolution of grain structure, crystallographic texture, and dislocation substructure was characterized in detail for a deformation temperature of 950 °C and strain rates of 0.1 and 10 s⁻¹, using electron backscatter diffraction and transmission electron microscopy. The hybrid modeling method utilizes a combination of empirical, physically-based, and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallization behavior is predicted from the measured microstructural state variables of internal dislocation density, subgrain size, and misorientation between subgrains using a physically-based model. The texture evolution is modeled using artificial neural networks.

Texture changes during uniaxial compression of Mg-3AI-1Zn

The mechanical anisotropy of wrought Mg alloys is very high. For example the yield stress of extruded Mg-3Al-1Zn tested in tension can be as high as twice that obtained in compression [1]. To solve the problems this creates for product design it is necessary to understand the sensitivity of texture to processing parameters. Uniaxial compression tests at different temperatures were performed on cylindrical samples of an extruded Mg-3Al-1Zn bar. The texture
during this deformation changes from a situation where all crystal c-axes are nearly perpendicular to the sample axis to one where the c-axes are all nearly parallel to this axis. Compression was stopped at different strains to examine the rate of this texture change. Textures were examined using EBSD measurements. It was found that different mechanisms operate depending on the temperature of deformation and that a variety of textures can be created. Also it was seen that an annealing treatment performed after compression has an influence on the texture. Afterwards the samples were subjected to another uniaxial compression test to examine the influence of texture on room temperature properties.

Learning to detect texture objects by artificial immune approaches

This paper introduces a novel method to detect texture objects from satellite images. First, a hierarchical strategy is developed to extract texture objects according to their roughness. Then, an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy. In this approach, texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection algorithm inspired by human immune system is employed to evolve antibodies. The population of antibodies is iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into the optimal antibody using the evolution principles of the clonal selection. Experimental results of texture object detection on satellite images are presented to illustrate the merit and feasibility of the proposed method.