Evaluation of grinding surface roughness based on color component difference of image

The current machine vision-based measurement of surface roughness of workpiece is mainly the evaluation index of gray image, while ignoring the characteristics of richer color information and strong sensitivity, this paper proposes a kind of information difference index of each color component of image to evaluate the grinding surface roughness. According to the different quality of virtual image of color blocks that are formed on grinding surfaces with different roughness, a reference image and a distortion image are selected, and the difference of each color channel information of the two images is calculated to measure the distortion degree of the image, so as to achieve the purpose of measuring the surface roughness. The experimental results show that the proposed indicators have certain feasibility and the method is simple.

[1]  Y I Huaian,et al.  Measuring grinding surface roughness based on the sharpness evaluation of colour images , 2016 .

[2]  B. Ramamoorthy,et al.  The influence of component inclination on surface finish evaluation using digital image processing , 2007 .

[3]  Y. S. Tarng,et al.  Surface roughness inspection by computer vision in turning operations , 2001 .

[4]  Rajneesh Kumar,et al.  Application of digital image magnification for surface roughness evaluation using machine vision , 2005 .

[5]  R. Dony,et al.  Edge detection on color images using RGB vector angles , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

[6]  Tao Sun,et al.  Origins for the size effect of surface roughness in diamond turning , 2016 .

[7]  Enhui Lu,et al.  A new surface roughness measurement method based on a color distribution statistical matrix , 2017 .

[8]  Ingmar Lissner,et al.  Image-Difference Prediction: From Grayscale to Color , 2013, IEEE Transactions on Image Processing.

[9]  E. S. Gadelmawla,et al.  A vision system for surface roughness characterization using the gray level co-occurrence matrix , 2004 .

[10]  Zhanqiang Liu,et al.  Tool wear behaviors and corresponding machined surface topography during high-speed machining of Ti-6Al-4V with fine grain tools , 2018 .

[11]  Hubert W. Schreier,et al.  Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications , 2009 .

[12]  Jian Liu,et al.  Designing indices to measure surface roughness based on the color distribution statistical matrix (CDSM) , 2018 .

[13]  Huaian Yi.  Detection Method of Grinding Surface Roughness Based on Image Definition Evaluation , 2016 .