Tamer omar mohamed diab
Asst. Professor
Forming Temperature Investigation of Aluminum and Aluminum/Silicon Carbide Using Image Texture Features
Machining, Joining and Modifications of Advanced Materials, Advanced Structured Materials • 2016
Publication Information
Authors
Ahmad E. Eladawi, Tamer O. Diab and Hammad T. Elmetwally
Keywords
Image processing Computer vision Sum and difference histogram
matrices GLSDM Texture features Aluminum-based composites Deformation temperature
Image processing Computer vision Sum and difference histogram
matrices,GLSDM , Texture features, Aluminum-based composites, Deformation temperature
Journal
Machining, Joining and Modifications of Advanced Materials, Advanced Structured Materials
Publisher
Not Available
Volume
61
Issue
Not Available
Pages
13
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
Abstract
The processing of aluminum composites plays a significant factor in
develop the usage of such composites. The analysis of microstructure of the
deformed composites specimens gives a degree of understanding of the effect of
deformation process conditions. In recent years, the advent of high-speed,
general-purpose, digital computers and vision systems has made image analysis
easier and more flexible. Computer vision technology has maintained tremendous
vitality in many fields. One of the common techniques employed by computer
vision is the texture features techniques. Texture is related to qualitative properties
of surfaces, but due to its complexity and great variety, there exists neither a unique
definition of texture nor an accepted computational representation of it. Image
texture analysis is useful in a variety of applications and has been a subject of
intense study by many researchers. In the present work, the effect of forming
temperature on the forming behavior of aluminum and aluminum reinforced by
10 wt% SiC was studied by using image texture features after using the compression test which was done at an elevated temperature range of 300–550 °C and a
constant strain rate of 0.024 s−1
. The current study used a computer vision technique with Sum and Difference Histogram Matrices (GLSDM) to investigate the
forming temperature of aluminum and aluminum/silicon carbide (Al and Al/Sic).
The relationships between each GLSDM texture feature and the operation temperatures are discussed and the correlation coefficients are obtained. The results
showed that some texture features have high correlation coefficients with high
sensitivity to changes in temperature.
develop the usage of such composites. The analysis of microstructure of the
deformed composites specimens gives a degree of understanding of the effect of
deformation process conditions. In recent years, the advent of high-speed,
general-purpose, digital computers and vision systems has made image analysis
easier and more flexible. Computer vision technology has maintained tremendous
vitality in many fields. One of the common techniques employed by computer
vision is the texture features techniques. Texture is related to qualitative properties
of surfaces, but due to its complexity and great variety, there exists neither a unique
definition of texture nor an accepted computational representation of it. Image
texture analysis is useful in a variety of applications and has been a subject of
intense study by many researchers. In the present work, the effect of forming
temperature on the forming behavior of aluminum and aluminum reinforced by
10 wt% SiC was studied by using image texture features after using the compression test which was done at an elevated temperature range of 300–550 °C and a
constant strain rate of 0.024 s−1
. The current study used a computer vision technique with Sum and Difference Histogram Matrices (GLSDM) to investigate the
forming temperature of aluminum and aluminum/silicon carbide (Al and Al/Sic).
The relationships between each GLSDM texture feature and the operation temperatures are discussed and the correlation coefficients are obtained. The results
showed that some texture features have high correlation coefficients with high
sensitivity to changes in temperature.