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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Prediction of Strain Rate of Aluminum/Silicon Carbide Using Gray Level Run-Length Matrices
Author(s)
Ahmad E. Eladawi1, Saad A. A. Sayed2, Hammad T. Elmetwally3 and Tamer O. Diab4
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DOI:10.17265/1934-8975/2017.05.008
Affiliation(s)
1. Department of Mechanical Engineering, Benha University, Banha 13511, Egypt
2. Department of Mechanical Design, Helwan University, Helwan 11731, Egypt
3. Department of Mechanical Engineering, Beni-Suef University, Beni-Suef 62511, Egypt
4. Department of Electrical Engineering, Benha University, Banha 13511, Egypt
ABSTRACT
The microstructural processes
occurring in metals and alloys during hot deformation are: DRX (dynamic
recrystallization), superplastic deformation, dynamic recovery, wedge cracking,
void formation, inter-crystalline cracking, prior particle boundary (FFB) cracking,
and flow instability processes. Deformation characteristics of materials are interpreted
as follows: in the low temperature
(T ≤ 0.25 of melting temperature) and high strain rate regime of 10 to 100 s-1,
void formation occurs at hard particles and leads to ductile fracture. Many researchers used
the currently defined statistical approaches to characterize images and extract
useful information from the captured images. For more suitable of specific tasks,
some researchers are introducing new texture features.
HOS (higher-order statistics) estimate properties of three or
more pixels occurring at specific locations relative to each other. GLRLMs (gray level run-length
matrices) are popular method of HOS to extract texture features. This paper deals
with texture features of GLRLM to predict strain rate values for Aluminum/Silicon
Carbide.
KEYWORDS
Image processing, computer vision, GLRLMs, texture features, strain rate.
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