Skip to main content
SpringerLink
Log in

A new improved Laws-based descriptor for surface roughness evaluation

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ciulli E, Ferreira L, Pugliese G, Tavares S (2008) Rough contacts between actual engineering surfaces: part I. Simple models for roughness description. Wear 264(11–12):1105–1115. doi:10.1016/j.wear.2007.08.024

    Article  Google Scholar 

  2. Lee BY, Yu SF, Juan H (2004) The model of surface roughness inspection by vision system in turning. Mechatronics 14(1):129–141. doi:10.1016/S0957-4158(02)00096-X

    Article  Google Scholar 

  3. Morala-Argüello P, Barreiro J, Alegre E, Suarez S, González-Castro V (2009) Qualitative surface roughness evaluation using Haralick features and wavelet transform. In: Katalinic B (ed) Ann. DAAAM proc. int. DAAAM symp., 1726–9679, vol 20. DAAAM International, Vienna, pp 1241–1242

    Google Scholar 

  4. Gadelmawla ES (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E Int 37(7):577–588

    Article  Google Scholar 

  5. Morala-Argüello P, Barreiro J, Alegre E, González-Castro V (2009) Application of textural descriptors for the evaluation of surface roughness class in the machining of metals. In: MESIC’09, conference proceedings, pp 833–839

  6. Luo XC, Cheng K, Ward R (2005) The effects of machining process variables and tooling characterisation on the surface generation: modelling, simulation and application promise. Int J Adv Manuf Technol 25:1089–1097

    Article  Google Scholar 

  7. Zhang X, Krewet C, Kuhlenktter B (2006) Automatic classification of defects on the product surface in grinding and polishing. Int J Mach Tools Manuf 46(1):59–69

    Article  Google Scholar 

  8. Haralick R (1978) Statistical and structural approaches to texture. In: Proceedings of the IEEE, pp 45–69

  9. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  10. Barreiro J, Castejón M, Alegre E, Hernández L (2008) Use of descriptors based on moments from digital images for tool wear monitoring. Int J Mach Tools Manuf 48(9):100–1013

    Article  Google Scholar 

  11. Castejón M, Alegre E, Barreiro J, Hernández L (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47(12-13):1847–1853. doi:10.1016/j.ijmachtools.2007.04.001

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Alegre E, Alaiz-Rodriguez R, Barreiro J, Ruiz J (2009) Use of contour signatures and classification methods to optimize the tool life in metal machining. Est J Eng 15(1):3–12. doi:10.3176/eng.2009.1.01

    Article  Google Scholar 

  14. Al-Kindi GA, Shirinzadeh B (2007) An evaluation of surface roughness parameters measurement using vision-based data. Int J Mach Tools Manuf 47(3–4):697–708. doi:10.1016/j.ijmachtools.2006.04.013

    Article  Google Scholar 

  15. Al-Kindi GA, Shirinzadeh B (2009) Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens. Image Vis Comput 27(4):444–458. doi:10.1016/j.imavis.2008.06.011

    Article  Google Scholar 

  16. Suarez S, Alegre E, Barreiro J, Morala-Argüello P, Gonzalez Castro V (2009) Classification and correlation of surface roughness in metallic parts using texture descriptors. In: Katalinic B (ed) Ann. DAAAM proc. int. DAAAM Symp., vol 20. DAAAM International, Vienna, pp 1293–1295

    Google Scholar 

  17. Laws K (1980) Rapid texture identification in. In: SPIE, image processing for missile guidance, vol 238, pp 376–380

  18. Laws K (1980) Textured image segmentation. Ph.D. thesis, dissertation, University of Southern California

  19. Ramapriya S, Srivatsa SK (2008) Estimation of surface roughness parameter using wavelets based feature extraction. IJCSNS 8(10):282–288

    Google Scholar 

  20. Grzesik W, Brol S (2009) Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials. J Mater Process Technol 209(5):2522–2531. doi:10.1016/j.jmatprotec.2008.06.009

    Article  Google Scholar 

  21. Li H, Torrance KE (2005) An experimental study of the correlation between surface roughness and light scattering for rough metallic surfaces. SPIE, Bellingham, p 58780V

  22. Suarez S, Alegre E, Morala-Argüello P, Barreiro J, González-Castro V (2008) Evaluación de diferentes tipos de iluminación para la clasificación de la rugosidad de piezas metálicas mediante análisis de imagen. XXIX Jornadas de Automática, Tarragona, Spain

  23. Zhongxiang H, Lei Z, Jiaxu T, Xuehong M, Xiaojun S (2009) Evaluation of three-dimensional surface roughness parameters based on digital image processing. Int J Adv Manuf Technol 40:342–348

    Article  Google Scholar 

  24. ISO 1302 (2002) Geometrical product specifications (GPS)—indication of surface texture in technical product documentation

Download references

Author information

Authors and Affiliations

  1. Departamento de Ingeniería Eléctrica y de Sistemas y Automática, Universidad de León, León, Spain

    Enrique Alegre & Sir Alexci Suárez-Castrillón

  2. Área de Ingeniería de los Procesos de Fabricación, Universidad de León, León, Spain

    Joaquín Barreiro

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. Sir Alexci Suárez-Castrillón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Enrique Alegre.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alegre, E., Barreiro, J. & Suárez-Castrillón, S.A. A new improved Laws-based descriptor for surface roughness evaluation. Int J Adv Manuf Technol 59, 605–615 (2012). https://doi.org/10.1007/s00170-011-3507-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3507-z

Keywords

Search

Navigation