Measurement of Surface Roughness by Computer Vision in Planning Operations

Xiang Wei Chen; Zhi Kui Zhang; Zhao Hui Liu

doi:10.4028/www.scientific.net/AMR.146-147.361

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 146-147Measurement of Surface Roughness by Computer...

Measurement of Surface Roughness by Computer Vision in Planning Operations

Abstract:

Detection and Recognition of the surface roughness in the images is a topic which has received a lot of attention in the field of image processing. In this paper, a new non-contact measurement method of surface roughness, by texture analysis, is developed based on Charge Coupled Device (CCD) image in planning operations. The surface image of the workpiece is first acquired using the A102f CCD digital camera. The image captured will be converted to others kinds of images (Binary, and Gray scale) to be suitable for the detection algorithms used for the different types of surface. The main Image processing approaches is used such as Smoothing process, Noise reduction, Edge detection, Region Splitting, and Hough Transform etc. The predicted surface finish values using this measurement method are found to correlate well with the conventional stylus surface finish ( Ra ) values.

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Periodical:

Advanced Materials Research (Volumes 146-147)

Pages:

361-365

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.146-147.361

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Online since:

October 2010

Authors:

Xiang Wei Chen, Zhi Kui Zhang, Zhao Hui Liu

Keywords:

Hough Transform (HT), Image Processing, Surface Roughness (SR), Texture Analysis

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References

[1] S. Damodarasamy, S. Raman, Texture analysis using computer vision, Computers in Industry 16 25–34 (1991).

DOI: 10.1016/0166-3615(91)90005-t

Google Scholar

[2] Hoy DEP, F Yu. Surface quality assessment using computer vision methods. Journal Materials Processing Technology 28: 265–274(1991).

DOI: 10.1016/0924-0136(91)90225-4

Google Scholar

[3] Median filtering, deterministic properties. In Huang T. S., editor, Two-Dimensional Digital Signal Processing, volume II. Springer Verlag, Berlin, (1981).

Google Scholar

[4] Milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing Analysis, and Machine Vision, Third Edition. Thomson Learning, America (2008).

DOI: 10.1117/12.256634

Google Scholar

[5] Tony Lindeberg, Edge Detection and Ridge Detection with Automatic Scale Selection., Department of Numerical Analysis and Computing Science, Royal Institute of Technology, International Journal of Computer Vision, S-10044 Stockholm, Sweden, Jan, (1996).

DOI: 10.1109/cvpr.1996.517113

Google Scholar