SpringerLink
Log in

Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes

  • Conference paper
Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

Included in the following conference series:

Abstract

This work presents a method to perform a surface finish control using a computer vision system. Test parts used were made of AISI 303 stainless steel and were machined with a MUPEM CNC multi-turret parallel lathe. Using a Pulnix PE2015 B/W camera, a diffuse illumination and a industrial zoom, 140 images were acquired. We have applied a vertical Prewitt filter to all the images obtaining two sets, the original one and the filtered. We have described the images using three different methods. The first features vector was composed by the mean, standard deviation, skewness and kurtosis of the image histogram. The second features vector was made up by four Haralick descriptors – contrast, correlation, energy and homogeneity. The last one was composed by 9 Laws descriptors. Using k-nn we have obtained a hit rate around 90 % with filtered images and, the best one, using Laws features vector of 92.14% with unfiltered images. These results show that it is feasible to use texture descriptors to evaluate the rugosity of metallic parts in the context of product quality inspection.

This work has been partially supported by the research project DPI2006-02550 from the Spanish Ministry of Education and Science and the ULE2005-01 from the University of León.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 128.39
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. ISO 4288:1996. Geometrical product specification (GPS) - Surface texture: Profile method - Rules and procedures for the assessment of surface texture

    Google Scholar 

  2. Jiang, X., Whitehouse, D.: Miniaturized optical measurement methods for surface nanometrology. Annals of the CIRP 55 (2006)

    Google Scholar 

  3. Al-Kindi, G.A., Shirinzadeh, B.: An evaluation of surface roughness parameters measurement using vision-based data. Intl. J. of Machine Tools & Manufacture 47, 697–708 (2007)

    Article  Google Scholar 

  4. Senin, N., Ziliotti, M., Groppetti, R.: Three-dimensional surface topography segmentation through clustering. Wear 262, 395–410 (2007)

    Article  Google Scholar 

  5. Schmahling, J., Hamprecht, F.A., Hoffmann, D.M.P.: A three-dimensional measure of surface roughness based on mathematical morphology. Intl. J. of Machine Tools & Manufacture 46, 1764–1769 (2006)

    Article  Google Scholar 

  6. Alegre, E., Barreiro, J., Fernández, R.A., Castejón, M.: Design of a computer vision system to estimate tool wearing. Material Science Forum 526, 61–66 (2006)

    Article  Google Scholar 

  7. Castejón, M., Alegre, E., Barreiro, J., Hernández, L.K.: On-line tool wear monitoring using geometric descriptors from digital images. Intl. J. of Machine Tools & Manufacture 47, 1847–1853 (2007)

    Article  Google Scholar 

  8. Josso, B., Burton, D.R., Lalor, M.J.: Frequency normalised wavelet transform for surface roughness analysis and characterisation. Wear 252, 491–500 (2002)

    Article  Google Scholar 

  9. Whitehead, S.A., Shearer, A.C., Watts, D.C., Wilson, N.H.F.: Comparison of two stylus methods for measuring surface texture. Dental Materials 15, 79–86 (1999)

    Article  Google Scholar 

  10. Tarng, Y.S., Lee, B.Y.: Surface roughness inspection by computer vision in turning operations. International Journal of Machine Tools and Manufacture 41, 1251–1263 (2001)

    Article  Google Scholar 

  11. Lee, B.Y., Yu, S.F., Juan, H.: The model of surface roughness inspection by vision system in turning. Mechatronics 14, 129–141 (2004)

    Article  Google Scholar 

  12. Kumar, R., Kulashekar, P., Dhanasekar, B., Ramamoorthy, B.: Application of digital image magnification for surface roughness evaluation using machine vision. International Journal of Machine Tools and Manufacture 45, 228–234 (2005)

    Article  Google Scholar 

  13. Juan, H., Lee, B.Y., Yu, S.F.: A study of computer vision for measuring surface roughness in the turning process. The International Journal of Advanced Manufacturing Technology 19, 295–301 (2002)

    Article  Google Scholar 

  14. Gadelmawla, E.S.: A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E international (NDT E int.) 37, 577–582 (2004)

    Article  Google Scholar 

  15. Ramana, K.V., Ramamoorthy, B.: Statistical methods to compare the texture features of machined surfaces 29, 1447–1459 (1996)

    Google Scholar 

  16. Krewet, B., Zhang, C., Kuhlenktter, X.: Automatic classification of defects on the product surface in grinding and polishing. International Journal of Machine Tools and Manufacture 46, 59–69 (2006)

    Article  Google Scholar 

  17. Singh, V., Mishra, R.: Develo** a machine vision system for spangle classification using image processing and artificial neural network. Surface and Coatings Technology 201, 2813–2817 (2006)

    Article  Google Scholar 

  18. Ikonen, L., Toivanen, P.J.: Distance and nearest neighbor transforms on gray-level surfaces. Pattern Recognition Letters 28, 604–612 (2007)

    Article  Google Scholar 

  19. Kassim, A.A., Mannan, M.A., Mian, Z.: Texture analysis methods for tool condition monitoring. Image Vision Comput. 25, 1080–1090 (2007)

    Article  Google Scholar 

  20. Ngan, C.C., Tam, H.Y.: A non-contact technique for the on-site inspection of molds and dies polishing. Journal of Materials Processing Technology 155-156, 1184–1188 (2004)

    Article  Google Scholar 

  21. Persson, U.: Surface roughness measurement on machined surfaces using angular speckle correlation. Journal of Materials Processing Technology 180, 233–238 (2006)

    Article  Google Scholar 

  22. Pernía-Espinoza, A.V., Ordieres-Meré, J.B., Martínez-De-Pisón, F.J., González-Marcos, A.: TAO–robust backpropagation learning algorithm. Neural Networks 18(2), 191–204 (2005)

    Article  Google Scholar 

  23. González Marcos, A., Pernía Espinoza, A.V., Alba Elías, F., García Forcada, A.: A neural network-based approach for optimising rubber extrusion lines. International Journal of Computer Integrated Manufacturing 20(8), 828–837 (2007)

    Article  Google Scholar 

  24. Martínez-de-Pisón, F.J., Barreto, C., Pernía, A.V., Alba, F.: Modelling of an elastomer profile extrusion process using support vector machines (SVM). Journal of Materials Processing Technology 197(1–3), 161–169 (2008)

    Article  Google Scholar 

  25. Halpern, J.Y.: Reasoning about uncertainty. MIT Press, Cambridge (2003)

    MATH  Google Scholar 

  26. Lee, K.C., Ho, S.J., Ho, S.Y.: Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precision Engineering 29, 95–100 (2005)

    Article  Google Scholar 

  27. Materka, A., Strzelecki, M.: Texture analysis methods a review. Technical report, Technical University of Lodz, Institute of Electronics, COST B11 report (1998)

    Google Scholar 

  28. Davis L. S.: Image texture analysis techniques - a survey. Technical Report CS-TR, pp. 80–139, 1 (1980)

    Google Scholar 

  29. Davis, L.S., Clearman, M., Aggarwal, J.K.: A comparative texture classication study based on generalized co-occurrence matrices. In: Conference (1979)

    Google Scholar 

  30. Laws, K.: Texture energy measures. In: Image Understanding Workshop, DARPA (1979)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad de León. E.I. Industrial e Informática, 24071, León, Spain

    Enrique Alegre, Joaquín Barreiro, Manuel Castejón & Sir Suarez

Authors
  1. Enrique Alegre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joaquín Barreiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Manuel Castejón
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sir Suarez
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Aurélio Campilho Mohamed Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alegre, E., Barreiro, J., Castejón, M., Suarez, S. (2008). Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_110

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69812-8_110

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation