Application of Gray Level Co-Occurrence Matrix Method in Characterization of Cylindrical Grinding Surface Roughness

Abstract:

Machine vision based surface roughness inspection method is applied to assess different cylindrical grinding surfaces under LED illumination. Images directly recorded by a camera are analyzed by gray level co-occurrence matrix (GLCM) method to discover its texture information. It shows obviously relationship between feature values of the matrix and their corresponding surface roughness values. Uniform table are also designed to choose optimal parameters, which is five of distance between pixel pairs and ninety degree of angle to calculate GLCM. Entropy is chosen to represent different surface roughness images by comparison of correlation coefficients between the parameters and the corresponding surface roughness values.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 2113-2116
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.2113
• Online since: October 2013
• Authors: Zi Xin Chen, Feng Yu Xu
• Keywords: Gray-Level Co-Occurrence Matrix, Machine Vision, Surface Roughness (SR), Texture Analysis

References

[1] C. J. TAY, S. H. Wang, C. Quan, et al, In situ surface roughness measurement using a laser scattering method, Optics Communications, 218(2003)1-10.

DOI: 10.1016/s0030-4018(03)01102-7

[2] V. Elango, L. Karunamoorthy, Effect of lighting conditions in the study of surface roughness by machine vision-an experimental design approach, The International Journal of Advanced Manufactured Technology, 37(2008)92-103.

DOI: 10.1007/s00170-007-0942-y

[3] F. Luk, V. Huynh, W. North, Measurement of surface roughness by a machine vision system, Journal of Physics E: Scientific Instruments, 22(1989)977-980.

DOI: 10.1088/0022-3735/22/12/001

[4] D. M. Tsai, J. J. Chen, J. F. Chert, A Vision System for Surface Roughness Assessment Using Neural Networks, International Journal of Advanced Manufactured Technology, 14(1998) 412-422.

DOI: 10.1007/bf01304620

[5] P. Priya, B. Ramamoorthy, The influence of component inclination on surface finish evaluation using digital image processing finish evaluation using digital image processing, International Journal of Machine Tools & Manufacture, 47(2007) 570-579.

DOI: 10.1016/j.ijmachtools.2006.05.005

[6] E. S. Gadelmawla, A vision system for surface roughness characterization using the gray level co-occurrence matrix, NDT&E International, 37(2004)577-588.

DOI: 10.1016/j.ndteint.2004.03.004

[7] S. Dutta, A. Datta, et al, Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique, Precision Engineering, 36(2012) 458-466.

DOI: 10.1016/j.precisioneng.2012.02.004

[8] D. Samik, D. Arpan, B. Kaustav, et al, Automatic characterization of fracture surfaces of AISI 304LN stainless steel using image texture analysis. Measurement, 45(2012)1140-1150.

DOI: 10.1016/j.measurement.2012.01.026