Multi-scale analysis of connectivity for image retrieval

Previously, we proposed the concept of connectivity to obtain discriminating shape descriptors. In this paper, we use connectivity to obtain superior distance histograms for multi-scale images. Experiments are performed to evaluate the distance histograms, based on connectivity, for shape-based retrieval of multi-scale images. Item S8 within the MPEG-7 still images content set is used for performing experiments. Experimental results show that the proposed method enhances retrieval performance significantly.

Multi-scale rheological model for discontinuous phenomena in materials under deformation conditions

A study of possibilities given by the developed Cellular Automata–Finite Element (CAFE) multi-scale model for prediction of the initiation and propagation of micro-shear bands and shear bands in metallic materials subjected to plastic deformation is described in the paper. Particular emphasis in defining the criterion for initiation of micro-shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of those phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi-scale model of strain localization. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the paper. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is presented in the paper as well. In this approach remeshing becomes possible and mesh distortion, which limits application of the CAFE method to simple deformation processes, is eliminated.

Comparison of the strain distribution obtained from multi scale and conventional approaches to modelling extrusion

An investigation of the application of a multi scale CAFE model to prediction of the strain localization phenomena in industrial processes, such as extrusion, is presented in this work. Extrusion involves the formation of a strong strain localization zone, which influences the final product microstructure and may lead to a coarse grain layer close to the surface. Modelling of the shape of this zone and prediction of the strain magnitude will allow computer aided design of the extrusion process and optimisation of the technological parameters with respect to the microstructure and properties of the products. Thus, the particular objective of this work is comparison of the FE and CAFE predictions of strain localization in the shear zone area in extrusion. Advantages and disadvantages of the developed CAFE model are also discussed on the basis of the simulation results.

Development of the multi-scale analysis model to simulate strain localization occurring during material processing

Abstract A detailed description of possibilities given by the developed Cellular Automata—Finite Element (CAFE) multi scale model for prediction of the initiation and propagation of micro shear bands and shear bands in metallic materials subjected to plastic deformation is presented in the work. Particular emphasis in defining the criterion for initiation of micro shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of these phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi scale model of strain localization phenomena. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the work. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is a subject of this work as well. In the developed model remeshing becomes possible and difficulties limiting application of the CAFE method to simple deformation processes are solved. Obtained results of numerical simulaA detailed description of possibilities given by the developed Cellular Automata—Finite Element (CAFE) multi scale model for prediction of the initiation and propagation of micro shear bands and shear bands in metallic materials subjected to plastic deformation is presented in the work. Particular emphasis in defining the criterion for initiation of micro shear and shear bands, as well as in defining the transition rules for the cellular automata, is put on accounting for the physical aspects of these phenomena occurring in two different scales in the material. The proposed approach led to the creation of the real multi scale model of strain localization phenomena. This model predicts material behavior in various thermo-mechanical processes. Selected examples of applications of the developed model to simulations of metal forming processes, which involve strain localization, are presented in the work. An approach based on the Smoothed Particle Hydrodynamic, which allows to overcome difficulties with remeshing in the traditional CAFE method, is a subject of this work as well. In the developed model remeshing becomes possible and difficulties limiting application of the CAFE method to simple deformation processes are solved. Obtained results of numerical simulations are compared with the experimental results of cold rolling process to show good predicative capabilities of the developed model.tions are compared with the experimental results of cold rolling process to show good predicative capabilities of the developed model.

Multi-scale modelling of DP590 steel

Dual Phase (DP) steel one of the Advanced High Strength Steels (AHSS) has a two phase microstructure where soft and hard phase acts together to offer a high strength composite effect. The high strength, however, must be balanced with ductility so that complex parts and designs can be manufactured from AHSS sheets. However, during forming certain grades of DP steel a sudden crack can occur without any intimation of necking. Thus, due to this abnormal forming behaviour, is difficult to accurately predict because most classical modelling approaches are not designed for such micro-structurally heterogeneous materials. These modelling approaches are generally based on an average representation of the material behaviour in a continuum mechanics formulation. This works for materials that are homogenous, or at least could be assumed to be homogenous at scales lower than the naked eye can see. However, for a material like AHSS, the microstructure plays a significant role in dictating the mechanical behaviour at the macro-scale. This paper studies the multi-scale modelling ofDP590 steel. It is found that the sufficient accuracy can be achieved from multi-scale modelling while comparing with the experiments.

Fracture of DP590 steel : a multi-scale modeling approach

Advanced high strength steel sheets are one of the higher strength advance material developed by the steel industry for automotive bodies. One of the categories of this advanced high strength steel is Dual Phase (DP) steel. This steel consists of a two phase microstructure where soft and hard phase acts together to offer a high strength composite effect. The combination of high strength and ductility exhibited by these sheets allows the design and manufacture of complex parts. However, during forming certain grades of DP steel sudden cracking can occur without any intimation of necking. This abnormal forming behavior is difficult to accurately predict because most classical modelling approaches are not designed for such micro-structurally heterogeneous materials. These modelling approaches are generally based on an average representation of the material behaviour in a continuum mechanics formulation. This works for materials that are homogenous, or at least could be assumed to be homogenous at scales lower than the naked eye can see. However, for a material like advanced high strength steel, the microstructure plays a significant role in dictating the mechanical behavior at the macro-scale. This paper studies the forming and fracture behavior through multi-scale modeling of DPO590 steel. It is found that the sufficient accuracy can be achieved from multi-scale modeling when comparing with experiments.

Mallee Emu-wren (Stipiturus mallee) : multi-scale habitat requirements and population structure

This multi-disciplinary investigation found that: i) in Triodia-mallee the Mallee Emu-wren requires vegetation greater than 16-18 years since last burned, with high coverage of mature growth-phase Triodia scariosa (spinifex) and, ii) the species is panmictic with relatively low genetic diversity and evidence of genetic drift and bottlenecks.

Multi-scale material modelling platform

The data includes material models suitable for modelling and simulation of multi-scale heterogeneous materials, as well as simulation results and experimental observations for verification and validation of simulated results.

Multi-scale local pattern co-occurrence matrix for textural image classification

Textural image classification technologies have been extensively explored and widely applied in many areas. It is advantageous to combine both the occurrence and spatial distribution of local patterns to describe a texture. However, most existing state-of-the-art approaches for textural image classification only employ the occurrence histogram of local patterns to describe textures, without considering their co-occurrence information. And they are usually very time-consuming because of the vector quantization involved. Moreover, those feature extraction paradigms are implemented at a single scale. In this paper we propose a novel multi-scale local pattern co-occurrence matrix (MS_LPCM) descriptor to characterize textural images through four major steps. Firstly, Gaussian filtering pyramid preprocessing is employed to obtain multi-scale images; secondly, a local binary pattern (LBP) operator is applied on each textural image to create a LBP image; thirdly, the gray-level co-occurrence matrix (GLCM) is utilized to extract local pattern co-occurrence matrix (LPCM) from LBP images as the features; finally, all LPCM features from the same textural image at different scales are concatenated as the final feature vectors for classification. The experimental results on three benchmark databases in this study have shown a higher classification accuracy and lower computing cost as compared with other state-of-the-art algorithms.