Deformation field and texture analysis in T-ECAP using a flow function

2021 ◽  
Vol 173 ◽  
pp. 110912
Author(s):  
A. Hasani ◽  
M. Sepsi ◽  
S. Feyzi ◽  
L.S. Toth
2005 ◽  
Vol 495-497 ◽  
pp. 1603-1608 ◽  
Author(s):  
Benoît Beausir ◽  
László S. Tóth ◽  
Olivier Bouaziz

Using a simple analytical flow function, an analysis of the deformation field in symmetrical rolling has been carried out. The so-obtained varying velocity gradient is incorporated into the Taylor polycrystal plasticity model to simulate the development of the deformation texture. The initial discontinuity in the deformation field of the entering material element on the flow lines is also taken into account. Multiple passes of the material is simulated. A strong texture gradient is obtained in good agreement with experiments carried out for rolling of plane carbon steel. It is shown that the shear component of the texture is strongly related to the nature of multiple passes of the rolling operation.


2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
A. Hasani ◽  
L. S. Toth ◽  
Sh. Mardokh Rouhani

One of the most applied severe plastic deformation processes is ECAP (equal-channel angular pressing) which is suitable to produce ultrafine-grained metallic materials with high mechanical performance. A variant of the ECAP process was proposed in 2009, which consists in reducing the diameter of the exit channel of the die; it is named the nonequal-channel angular pressing (NECAP) process. A flow line function was also proposed to describe the material flow and the deformation field during NECAP. In the present work, an improved version of that flow function is presented containing two additional parameters compared to the previously proposed function. The new parameters permit to control precisely the shapes and the positions of the flow lines. The new flow function was applied to 90° NECAP of commercially pure aluminum to characterize the deformation field and the extent of the plastic deformation zone. The crystallographic texture evolution is also simulated using the new function. Excellent agreements with experiments were obtained for both the flow line trajectories and the crystallographic texture.


2008 ◽  
Vol 14 (2-3) ◽  
pp. 89-164 ◽  
Author(s):  
Birgitte Nielsen ◽  
Fritz Albregtsen ◽  
Havard E. Danielsen

Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.


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