جستجوی مقالات مرتبط با کلیدواژه "texture" در نشریات گروه "فنی و مهندسی"

In the present study, a combination of X-ray diffraction spectroscopy, field emission scanning electron microscopy (FESEM) and optical microscopy methods were used to investigate the microstructure and crystallographic texture changes of API 5L X70 pipeline steel following an extreme cold working process that resulted in reduce the thickness of 80% in the sample. All investigations were done on two different sections of the sample, including the surface and the inclined section rotated 45 degrees relative to the surface. In fact, these two cross-sections are chosen to show the angle between the two important crystallographic planes of {100} and {110}. FESEM and optical microscope analyzes showed that the microstructure of the primary steel includes granular bainite, conventional upper bainite, and martensite-austenite islands. The results showed that the morphology of the initial microstructure consisted of mostly equiaxed grains, which after applying cold work due to being subjected to severe pressure, turns into a microstructure with the morphology of elongated grains, especially in the rotated section. Analysis of crystal orientation changes by X-ray diffraction spectroscopy showed that the density of {100} crystallographic planes decreased due to applied cold work on the surface and at the same time the density of {110} crystallographic planes on the surface increased. Also, the results proved that {100} crystallographic planes were replaced by {110} crystallographic planes on the sample rotated by 45 degrees.

Precise prediction of the texture component and simulation of the microstructure evolution facilitate the control and design of the final mechanical and physical properties. Through coupling the finite element simulation and crystal plasticity modelling, the current study introduced a robust technique for predicting the texture component after warm rolling. The simulation was then performed at two temperatures of 300 and 500oC for warm rolling. To calculate the appropriate hardening parameters for the crystal plasticity simulation, the experimental flow curves were obtained from torsion tests at the same temperatures of warm rolling. The presented framework predicted the texture components and associated intensities accurately. This was confirmed followed by comparing the results with the experimental ones. The proposed approach also predicted the flow curves correctly and precisely, as further proved by comparing the simulated flow curve based on the experimental flow curves.  Revealing the effect of deformation gradient on the texture evolution, the simulation also showed that the shear components imposed by friction rotated the texture components along the ND direction of the specimen.

Composites containing glass fiber reinforcement are used in important industries such as aerospace, automotive, and petrochemical due to their low weight, high abrasion resistance, and excellent resistance to ultraviolet light. Achieving a part in the desired shape and with a suitable quality surface is the main challenge for researchers and machining specialists. Due to the heterogeneous and anisotropic structure of composite materials reinforced with glass fibers, the machining of these materials often leads to failures such as matrix cracking, fiber pull-out, delamination, etc. on the surface of the workpiece. The failure behaviors are not caused by the heterogeneous and anisotropic structure of the composite but rather by the machining methods and the interaction between them. One of the most influential factors on the surface quality of fiber-reinforced composite parts is fiber orientation, which is studied in this research. Examining the orientation of glass fibers in 3 directions of 0 (zero), 45, and 90 degrees on the structure of the final surface of the epoxy-fiber composite in this research showed that the roughness of the surface increases with the increase of the fiber angle from zero to 90 degrees. Also, the obtained results showed that the delamination of the workpiece surface in the direction of 45 degrees is greater than in the two directions of 90 and zero degrees.

Due to the progress of the digital image world and increasing numbers, preparing a system for image retrieval is essential. A content-based image retrieval system should find similar images to the image search by a user. In this paper, a novel content-based image retrieval system is proposed. Considering the importance of texture in an image, we introduce a new feature as the histogram of the texture difference in the equal edge orientation. Then, the expressed features are extracted from training images in the proposed system. Then these features are learned using a support vector machine. The proposed system is examined using the standard WANG database. The results show the efficiency of the proposed system in retrieving images compared to similar methods.

In recent years, image annotation is one of the active research topics. In this article, a semi-supervised cooperative clustering technique is proposed for image annotation. Clustering methods are very popular because they do not require annotations. In order to achieve the highest efficiency, the clustering results of six systems with different color space and similarity criteria are cooperatively combined with the majority vote. When the number of votes for an image is low, relevant feedback is used to annotate it. One of the most important parts of the image retrieval system and clustering algorithm is determining the appropriate similarity criteria between images. Nowadays, the linear similarity criterion is mostly used to determine the similarity between images, but the nonlinear models can have much better performance due to their proximity to the human vision system, for this purpose, the KMRBF nonlinear similarity criterion is used to simulate vision. Humans and improvement of recovery results are suggested. Experiments on the Corel image database and satellite images show that the proposed method has good performance. According to the results obtained in the satellite image database, the YIQ color space has a higher accuracy (82.5%). Also, the three color spaces CIELab, HSV and YIQ have higher efficiency, because in these color spaces, luminance is separated from chrominance and these color spaces are closer to the human vision system.

In the present study, in addition to reviewing the severe plastic deformation (SPD) processes and their mechanisms in the microstructural evolution, previous researches on the corrosion behavior of various metallic materials that have undergone SPD are investigated. Grain size, as an important metallurgical parameter, affects a broad range of mechanical, chemical, physical and electrochemical properties of metallic materials. As mostly reported, the reduction in grain size is in line with a considerable improvement in the mentioned properties. In recent years, SPD methods have been nominated as an ideal way to reach nano- and ultrafine-grains. Among the mentioned properties, the corrosion performance of ultrafine-grained materials produced by SPD methods is still controversial for researchers. The purpose of this study is to review the SPD methods, the mechanism of grain refinement during them, and their effects on the corrosion behavior of various metals and engineering alloys such as steels, aluminum, titanium, copper, and magnesium.

In this paper we describe a novel noise-robust texture classification method using joint multiscale local binary pattern. The first step in texture classification is to describe the texture by extracting different features. So far, several methods have been developed for this topic, one of the most popular ones is Local Binary Pattern (LBP) method and its variants such as Completed Local Binary Pattern, Extended Local Binary Pattern, Local Temporary Pattern, Local Contrast Pattern, etc. In order to extract the features of a texture in different scales, the LBP method can be implemented in a multi-scale framework. For this purpose, the extracted feature vectors at different scales are usually concatenated together to produce the final feature vector with a longer length. But such a scheme has two main shortcomings. First, the LBP method is very sensitive to noise, hence by adding noise to a texture image, its feature vectors may change significantly. Second, by increasing the number of the scales, the length of the final feature vector is increased accordingly. This action increases the classification process time, and it may reduce the classification accuracy. To mitigate these shortcomings, this paper presents a method based on multiscale LBP, which has a better resistance against white Gaussian noise, while the length of its final feature vector is equal to the length of the final feature vector produced by the original LBP method. To implement the proposed method, we used 17 circular binary masks that contain 8 directed first-order masks, 8 directed second-order masks and 1 undirected mask. These masks have positive and negative weightes and each group of these masks have different radius which after convolution with input image extract features in different scales. Experiments were performed on four test groups of Outex database. Experimental results show that the proposed method is superior to the existing state-of-the-art methods. The complexity of proposed method is also analyzed. The results show that in this method, despite obtaining excellent classification accuracy, the complexity of the method has not changed much and even its complexity is less than some of the existing state-of-the-art methods.

The ability to recognize color, texture, and shape in image processing technology has led to the development of machine vision systems in various fields of agriculture, conversion industries, and industry. The existence of impurities and seeds with an unsuitable appearance in chickpeas and the direct effect of the appearance quality of the product on its marketability, shows the need for grading this product. The aim of the present study was to distinguish impurities and chickpeas with an inappropriate appearance from chickpeas with a suitable appearance by developing a machine vision system. Totally 270 images including 54 samples of chickpeas with suitable appearance and 36 samples of each type of chickpeas with inappropriate appearance (wrinkled, green, brown, and split) and foreign materials (stone and stem) were prepared. After preparing the sample images, the pre-processing and feature extraction steps were performed automatically and different color, texture and shape properties were extracted by developing and using an image processing algorithm. An algorithm was developed to select efficient features from the extracted features. Efficient features were classified by the artificial neural network method with total accuracy of 91.9%. The detection accuracy for desirable, wrinkled, cotyledon, immature, and brown chickpea and stem and stone impurities was equal to 98.1, 83.3, 100.0, 91.7, 97.2, 77.8, and 97.2% respectively. Using the developed system, the chickpea product can be graded with high accuracy and low cost, so that after separating the impurities, the desirable and undesirable chickpeas can be separated and sent to the market for different uses.

Flow line model as an efficient model in ECAP process analysis is based on functions that evaluate the material plastic flow inside the die. Among the various flow functions, the general flow function has the highest ability in analyzing of ECAP process. All parameters of this function have so far been considered as fixed parameters in calculating velocity and velocity gradient fields. Previous studies have shown a relatively linear relationship between the power in the function and the initial position of the flow lines. Accordingly, in the present work, the mentioned fields have been calculated based on the variable power and in order to investigate its effect, the experimental results of the ECAP process with an angle of 90 degree of aluminum alloy AA2124 have been used. The experimental flow lines were analyzed to find the linear relationship between the function’s power and the initial position of the flow lines. Simulations of the texture evolution were carried out subsequently using the general flow function in both constant and variable power conditions. Comparison of the simulated textures with the experimental results showed a much better performance of the variable power mode; in such a way that compared to the constant power condition, the deviation in the position of the simulated texture components in the variable power mode is significantly lower. However, the change in the power mode of the function did not show a significant effect on the intensity of the simulated texture.

In this research, the effects of ultrasound variables including power (120 to 360 W), time (1 to 6 min) and temperature (25 to 65 °C) on the average size of fat globules, Rheological properties (hardness, adhesiveness and consistency) and color parameters (whiteness index) of camel milk cream were investigated , Then, these properties were optimized using Box-Benken design models and Particle Swarm Optimization and and hill climbing algorithm (Numerical Optimization in Design Expert software). The results of the evaluation of the measured properties showed that with increasing power, time and temperature of ultrasonication, the average size of the fat globules decreased. The results of the back-extrusion test showed that only the power-time interaction at 95% confidence level had significant effect on the hardness and adhesiveness of the samples. So that the hardness and adhesiveness of the samples increased with increasing the time in low powers. The results also showed that with increasing the power and time of ultrasonication, whiteness index of samples increased. To compare the optimization performance of two-particle swarm and hill climbing algorithms, the average of 5 optimal points obtained from the two algorithms were compared by t-test. The results of t-test. showed that the particle swarm algorithm significantly achieved higher power and temperature and lower time, lead to significant decrease in average size of fat globules and increase in hardness. Therefore, it can be concluded that the particle swarm algorithm performs was better than the hill climbing algorithm in optimizing the models obtained from Box-Benken design in optimizing the homogenization conditions of camel milk cream.

The purpose of this study is to model the bread texture by acoustical- mechanical method, non- destructively. To do so, bread texture enriched with 3 different levels of modified chia (2.5%, 5% and 7.5%) was evaluated by texture analyzer at a test speed of 3 mm. s-1 to 30% compression, while the microphone was located at 5 cm from the samples at a 45° angle to the horizon. From the sound recorded during loading mean sound intensity, maximum of sound, variance, standard deviation, root mean absolute value, root mean square value, skewness, kurtosis, fifth moment, sixth moment, energy, entropy in time domain, and spectral entropy and natural frequency in the frequency domain were extracted. After selecting the most suitable features (maximum sound, variance, standard deviation, root mean absolute value, energy, entropy and natural frequency), based on statistical analysis, artificial neural network with 3 algorithms ( Marquardt, scaled conjugate gradient, gradient descent) was trained and tested with 7 neurons in the input layer (in accordance with the selected features) and 3 neurons in the output layer (hardness, gumminess, chewiness). Based on the results, the training error in the Levenberg - Marquardt algorithm was lower than the other algorithms, and the root mean squared error of the test stage of this algorithm to predict hardness, gumminess and chewiness were 0.14, 0.23, and 0.33, respectively. This shows the ability of the proposed method in predicting the quality of bread.