Characterization of the Cutting Conditions in Turning Operation Using the Gray Levels CO-Occurrence Matrix. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 7, Volume 34, Issue 3, September 2009, Page 10-21 PDF (551.7 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2021.157684 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Elamir Gadelmawla 
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| Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, 35516 Mansoura, Egypt | ||||
| Abstract | ||||
| The term texture features is considered a basic issue in image processing and computer vision because it is related to the qualitative properties of surfaces. Image texture analysis is useful in a variety of applications and has been a subject of intense study in many applications such as metal surface analysis, textiles characterization, ultrasonic images processing, and food qualities evaluation. One of the most common methods for texture analysis is the gray level co-occurrence matrix (GLCM), which has been widely used in industries because it has a large number of texture features that can be used to describe object textures. This paper introduces an application of the image texture features to characterize the effect of changing the cutting conditions in turning operations (feed, speed and depth of cut). A set of turning specimens with different cutting conditions were used for the characterization process. A vision system was employed to capture images for the specimens under investigation, then the images were analyzed using special software, which has been fully developed in-house to calculate all available texture features from the captured images. The correlation coefficients between each texture feature and the three cutting conditions were calculated and discussed. The results showed that eight texture features have good correlations with the feed; five have good correlations with the speed, while no texture features found to be have good correlations with the depth of cut. | ||||
| Keywords | ||||
| Computer vision; Co-occurrence matrix; Cutting conditions; Texture features; Turning | ||||
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