Investigation of the cutting conditions in milling operations using image texture features

Texture is an important image feature in image analysis, which is related to qualitative properties of surfaces and corresponds to both brightness value and pixel locations. Image texture has been introduced into a wide range of 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 grey level co-occurrence matrix (GLCM), which has a large number of texture features. In this work, an investigation of the relationship between GLCM texture features and the cutting conditions in milling operations (typically, feed, speed, and depth of cut) has been carried out. A vision system was employed to capture images for specimens with various known cutting conditions; then, the images were analysed by a software, which has been fully developed in-house to calculate 22 texture features. The relationship between each texture feature and the three cutting conditions are discussed and the correlation coefficients are introduced. The results showed that 15 texture features have good correlations with the feed, nine have good correlations with the speed, while only two have good correlations with the depth of cut.