scholarly journals Algorithm for quantifying the particle size distribution of non-metallic inclusions formation in steel production

10.30544/776 ◽  
2021 ◽  
Vol 27 (4) ◽  
pp. 437-447
Author(s):  
Marija Mihailović ◽  
Karlo Raić
Keyword(s):  
Size Distribution ◽  
Distribution Curve ◽  
Steel Production ◽  
Total Content ◽  
Limiting Factors ◽  
Quantitative Characterization ◽  
Metallic Inclusions ◽  
Routine Procedures ◽  
Inclusions In Steel

When the quantitative characterization of non-metallic inclusions in steel is done and the effect of limiting factors is assessed, and based on that the possibility of reconstruction of the total content of non-metallic inclusions in steel is estimated, further considerations can be directed towards predicting the model of size distribution curve. The aim of this work is to establish relations on the basis of which it will be possible to quantify the content of non-metallic inclusions in extra-pure steels, when metallographic control is difficult or even impossible by routine procedures.

Application of new techniques for characterization of non-metallic inclusions in steel

1995 ◽  
Vol 66 (4) ◽  
pp. 172-177 ◽  
Author(s):  
Yoshimoto Wanibe ◽  
Takashi Itoh ◽  
Kazushige Umezawa ◽  
Hiroshi Nagahama ◽  
Yoshio Nuri
Keyword(s):  
New Techniques ◽  
Metallic Inclusions ◽  
Inclusions In Steel

Fast Scanning Laser-OES. I. Characterization of Non-metallic Inclusions in Steel

2003 ◽  
Vol 36 (3) ◽  
pp. 659-665 ◽  
Author(s):  
Heinz-Martin Kuss ◽  
Horst Mittelstädt ◽  
Gregor Müller ◽  
Cetin Nazikkol
Keyword(s):  
Scanning Laser ◽  
Metallic Inclusions ◽  
Inclusions In Steel

scholarly journals IMPACT OF INTERSTITIAL ALUMINIUM CONTENT ON OCCURRENCE OF NON-METALLIC INCLUSIONS IN STEEL

2016 ◽  
Vol 22 (2) ◽  
pp. 88
Author(s):  
Vladimir Rega ◽  
Marek Molnár ◽  
Marek Šolc ◽  
Branislav Buľko ◽  
Peter Demeter ◽  
...  
Keyword(s):  
High Voltage ◽  
Liquid Steel ◽  
Steel Production ◽  
Emission Spectrometry ◽  
Aluminium Content ◽  
Metallic Inclusion ◽  
Metallic Inclusions ◽  
Metal Volume ◽  
Inclusions In Steel

<p class="AMSmaintext"><span lang="EN-GB">This paper details the study of interstitial aluminium impact on non-metallic inclusion occurrence on the basis of individual samples examination taken during steel production and processing. The aim was to identify and describe the relation between the content of interstitial aluminium in metal volume and the concentration of non-metallic inclusions on AlxOy basis occurring in steel. The identification of the non-metallic inclusions occurring in liquid steel was within this study implemented by the (AES) method based on the atom emission spectrometry evaluating the presence of the respective elements in the metal volume. This method works on a principle of emitted light from the existing source with a high voltage spark. The results of this study indicate a correlation between the interstitial aluminium content and concentrations of non-metallic inclusions on the AlxOy basis.</span></p>

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

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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