Grinding surface roughness measurement based on the spatial filtering of speckle pattern texture

被引：0
作者：
Wang Q. [1 ]
Lu R. [1 ]
Yang L. [1 ]
Lei L. [1 ]
机构：
[1] School of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei
来源：
Guangxue Xuebao/Acta Optica Sinica | 2010年 / 30卷 / 08期
关键词：
Spatial filtering; Speckle pattern; Surface roughness; Texture analysis;
D O I：
10.3788/AOS20103008.2324
中图分类号：
学科分类号：
摘要：
Surface speckle pattern intensity distribution resulting from laser light scattering from a rough surface contains various information about the geometrical and physical properties of the surface. A texture analysis method based on spatial filtering is used to analyze speckle patterns of grinding surface. The feature parameters of speckle texture with a good monotonic relation related to the surface roughness (Ra) are extracted. The basic principle of the texture analysis is to extract three types of vectors based on fractional Brownian motion model of window speckle images, which are the normalized scale range vector, the normalized pixel pair number vector, and the normalized multiscale intensity difference (NMSID) vector, then to make a NMSID vector transformation for speckle patterns, and finally statistically to investigate both the transformed images with zero gray pixels and the transformed images without zero gray pixels. The analysis results show that both texture features energy and new entropy of the transformed images have a good monotonic relation with surface roughness value Ra.
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页码：2324 / 2328
页数：4
相关论文
共 17 条
  • [1] White D.J., Stylus contact method for surface metrology in the ascendancy, Meas · Control, 31, 2, pp. 48-50, (1998)
  • [2] Bennett J.M., Recent development in surface roughness characterization, Meas. Sci. Technol., 3, 12, pp. 1119-1127, (1992)
  • [3] Gupta M., Raman S., Machine vision assisted characterized of machined surface, Int. J. Prod. Res., 39, 4, pp. 759-784, (2001)
  • [4] Sun D., Wu Q., Liu C., Et al., Application of laser scanning confocal microscopy in roughness characterization of worn surface, Chinese J. Lasers, 35, 9, pp. 1410-1414, (2008)
  • [5] Hou H., Shen J., Zhang D., Et al., Study on surface roughness of substrates under different cleaning techniques by total integrated scatter, Chinese J. Lasers, 36, 6, pp. 1560-1562, (2009)
  • [6] Ruffing B., Application of speckle-correlation methods to surface-roughness measurement: a theoretical study, J. Opt. Soc. Am. A, 3, 8, pp. 1297-1304, (1986)
  • [7] Chang S.I., Ravathur J.S., Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology, Quality Engineering, 17, 3, pp. 435-451, (2005)
  • [8] Kumar R., Kulashekar P., Dhanasekar B., Et al., Application of digital image magnification for surface roughness evaluation using machine vision, Int. J. of Machine Tools and Manufacture, 45, 2, pp. 228-234, (2005)
  • [9] Kiran M.B., Ramamoorthy B., Radhakrishnan V., Evaluation of surface roughness by vision system, Int. J. of Machine Tools and Manufacture, 38, 5-6, pp. 685-690, (1998)
  • [10] Leonard L.C., Toal V., Roughness measurement of metallic surfaces based on the laser speckle contrast method, Opt. & Lasers in Eng., 30, 5, pp. 433-440, (1998)
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