Skip to main content

Evaluation of Machine Learning Techniques for Classification of Surface Roughness of Machined Samples using Laser Speckle Imaging Technique

  • Conference paper
  • First Online:
Pattern Recognition (ICPR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15327))

Included in the following conference series:

  • 122 Accesses

Abstract

This study uses machine learning techniques to classify the surface roughness using the laser speckle images of the machined samples, an intriguing yet relatively less explored field of research in the realm of speckle metrology. The laser speckle imaging technique is sensitive to surface roughness paving the way for the classification of the machined specimen based on surface roughness using the distinct speckle pattern. The paper presents the analysis of the performance of the state-of-the-art machine learning techniques on the preliminary dataset of the speckle pattern of the machined sample. The gray level co-occurrence matrix is used for feature extraction. The model performance with various combinations of features is studied to distinguish the most descriptive feature for generalization. The assessment of the classifiers’ performance aids in the generalization of the classification and prediction of the roughness classes in the range \(R_a =0.1\ \mu m-1.6\ \mu m\) using the speckle images.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • 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

Similar content being viewed by others

References

  1. A.L.P.Camargo, M.R.B.Dias, M.R.Lemos, M.M.Mello, L. da Silva, P.A.M.dos Santos, J.A.O.Huguenin: Estimation of statistical properties of rough surface profiles from the hurst exponent of speckle patterns. ppl. Opt 59, 5957–5966 (2020)

    Google Scholar 

  2. Baradit, E., Gatica, C., Yáñez, M., Figueroa, J.C., Guzmán, R., Catalán, C.: Surface roughness estimation of wood boards using speckle interferometry. Opt. Lasers Eng. 128, 106009 (2020). https://doi.org/10.1016/j.optlaseng.2020.106009

    Article  Google Scholar 

  3. Bengio, Y., Delalleau, O., Le Roux, N.: The curse of dimensionality for local kernel machines. Techn. Rep 1258(12), 1 (2005)

    Google Scholar 

  4. Chebrolu, V., Koona, R., Raju, R., et al.: Automated evaluation of surface roughness using machine vision based intelligent systems. Journal of Scientific & Industrial Research 82(1), 11–25 (2022)

    Google Scholar 

  5. Chen, W., Zou, B., Li, Y., Huang, C.: A study of a rapid method for detecting the machined surface roughness. The International Journal of Advanced Manufacturing Technology 117, 3115–3127 (2021)

    Article  Google Scholar 

  6. Conners RW, H.C.: A theoretical comparison of texture algorithms. IEEE Trans Pattern Anal Mach Intell. 2(3):204-22, 110–118 (Mar 1980). 10.1109/tpami.1980.4767008

    Google Scholar 

  7. Corrêa, R.D.,Meireles, J.B.Huguenin J, Caetano D.P, Silva L: Fractal structure of digital speckle patterns produced by rough surfaces. Physica A: Statistical Mechanics and its Applications 392, 869–874 (02 2013). 10.1016/j.physa.2012.10.023

    Google Scholar 

  8. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  9. Kayahan, E., Oktem, H., Hacizade, F., Nasibov, H., Gundogdu, O.: Measurement of surface roughness of metals using binary speckle image analysis. Tribol. Int. 43(1), 307–311 (2010). https://doi.org/10.1016/j.triboint.2009.06.010

    Article  Google Scholar 

  10. Ghosh, A.K.: On optimum choice of k in nearest neighbor classification. Computational Statistics & Data Analysis 50(11), 3113–3123 (2006)

    Article  MathSciNet  Google Scholar 

  11. Haralick, R.M., Shanmugam, K., Dinstein: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6), 610–621 (1973). 10.1109/TSMC.1973.4309314

    Google Scholar 

  12. Hearst, M., Dumais, S., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intelligent Systems and their Applications 13(4), 18–28 (1998). https://doi.org/10.1109/5254.708428

    Article  Google Scholar 

  13. Hitoshi Fujii, T.A.: Roughness measurements of metal surfaces using laser speckle. J. Opt. Soc. Am. 67(9), 1171–1176 (1977)

    Article  Google Scholar 

  14. Hurden, A.: Vibration mode analysis using electronic speckle pattern interferometry. Optics & Laser Technology 14(1), 21–25 (1982)

    Article  Google Scholar 

  15. Jeyapoovan, T., Murugan, M.: Surface roughness classification using image processing. Measurement 46(7), 2065–2072 (2013)

    Article  Google Scholar 

  16. J.W.Goodman: Some fundamental properties of speckle\(\ast \). Journal of the Optical Society of America (1917-1983) 66(11), 1145–1150 (Nov 1976)

    Google Scholar 

  17. J.W.Goodman: Speckle Phenomena in Optics Theory and Applications Second Edition (2014)

    Google Scholar 

  18. Lehmann, P.: Surface-roughness measurement based on the intensity correlation function of scattered light under speckle-pattern illumination. Appl. Opt. 38(7), 1144–1152 (1999). https://doi.org/10.1364/AO.38.001144

    Article  Google Scholar 

  19. Liu, H., Zhang, S., Zhao, J., Zhao, X., Mo, Y.: A new classification algorithm using mutual nearest neighbors. In: 2010 Ninth International Conference on Grid and Cloud Computing. pp. 52–57. IEEE (2010)

    Google Scholar 

  20. Patil, S.H., Kulkarni, R.: Surface roughness measurement based on singular value decomposition of objective speckle pattern. Opt. Lasers Eng. 150, 106847 (2022). https://doi.org/10.1016/j.optlaseng.2021.106847

    Article  Google Scholar 

  21. Song, Y.Y., Ying, L.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiatry 27(2), 130 (2015)

    Google Scholar 

  22. Suhail, S.M., Ali, J.M., Jailani, H.S., Murugan, M.: Vision based system for surface roughness characterisation of milled surfaces using speckle line images. In: IOP Conference Series: Materials Science and Engineering. vol. 402, p. 012054. IOP Publishing (2018)

    Google Scholar 

  23. Taunk, Kashvi De, Sanjukta Verma, Srishti Swetapadma, Aleena: A brief review of nearest neighbor algorithm for learning and classification. In: 2019 international conference on intelligent computing and control systems (ICCS). pp. 1255–1260. IEEE (2019)

    Google Scholar 

  24. Tsai, D.M., Tseng, C.F.: Surface roughness classification for castings. Pattern Recogn. 32(3), 389–405 (1999)

    Article  Google Scholar 

  25. Wang, Y., Cao, J., Xu, C., Cheng, Y., Cheng, X., Hao, Q.: Moving target tracking and imaging through scattering media via speckle-difference-combined bispectrum analysis. IEEE Photonics J. 11(6), 1–14 (2019)

    Google Scholar 

  26. Wu, X, Kumar V: The Top Ten Algorithms in Data Mining 1st Edition (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shanta Hardas Patil .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Patil, S.H. (2025). Evaluation of Machine Learning Techniques for Classification of Surface Roughness of Machined Samples using Laser Speckle Imaging Technique. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15327. Springer, Cham. https://doi.org/10.1007/978-3-031-78398-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-78398-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78397-5

  • Online ISBN: 978-3-031-78398-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics