Advertisement

Evaluation of tool scraping wear conditions by image pattern recognition system

  • Wen-Yuh Jywe
  • Tung-Hsien Hsieh
  • Po-Yu ChenEmail author
  • Ming-Shi Wang
  • Yu-Tso Lin
ORIGINAL ARTICLE
  • 25 Downloads

Abstract

An image pattern recognition system, consisting of a CMOS camera, a set of illumination devices, and a lab-developed machine vision image analysis software, has been developed for evaluation of tool scraping wear conditions. The images of the scraped surface texture on the workpiece were generated by lab-developed automatic scraping machine under controlled conditions; the image was first transformed by Haar wavelet transform with Otsu’s algorithm to discriminate the vertical details of surface patterns, and they were then emphasized by weighted calculations according to tool scraping wear conditions. The experimental results show that the average difference between the data obtained from the surface image analysis and the roughness measurement is about ± 2%, proving the feasibility of the proposed system. Furthermore, a statistic analysis reveals that the standard deviation of the non-zero proportion of the medium frequency domain obtained by image processing might be considered a significant reference for tool replacement. For the field applications, the proposed system provides the capability of online scraping tool wear evaluation for scraping process in manufacturing site, compared with the traditional scraping tool wear evaluation method, which was time consuming; user needs to remove the workpiece to measure instrument.

Keywords

Image pattern recognition Machine vision Tool scraping wear Surface texture 

Notes

Funding information

The present work was funded by the Ministry of Economic Affairs (MoEA): 101-EC-17-A-05-S1-188 and the Ministry of Science and Technology (MoST) of Taiwan (Republic of China): 101-2623-E-150-003-IT; the authors would like to gratefully express their sincere acknowledgment to MoEA and MoST.

References

  1. 1.
    Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34:55–72CrossRefGoogle Scholar
  2. 2.
    Kiran MB, Ramamoorthy B, Radhakrishnan V (1998) Evaluation of surface roughness by vision system. Int J Mach Tools Manuf 38:685–690CrossRefGoogle Scholar
  3. 3.
    Mannan MA, Kassim AA, Jing M (2000) Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recogn Lett 21:969–979CrossRefGoogle Scholar
  4. 4.
    Li P-Y, Hao C-Y, Zhu S-W (2007) Machining tools wear condition detection based on wavelet packet. In: Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, pp. 1559-1564.Google Scholar
  5. 5.
    Kassim AA, Mannan MA, Mian Z (2007) Texture analysis methods for tool condition monitoring. Image Vis Comput 25:1080–1090CrossRefGoogle Scholar
  6. 6.
    Morala-Argüello P, Barreiro J, Alegre E (2011) An evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain. Int J Adv Manuf Technol 59:213–220CrossRefGoogle Scholar
  7. 7.
    Dutta S, Datta A, Chakladar ND, Pal SK, Mukhopadhyay S, Sen R (2012) Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precis Eng 36:458–466CrossRefGoogle Scholar
  8. 8.
    Gadelmawla ES, Al-Mufadi FA, Al-Aboodi AS (2014) Calculation of the machining time of cutting tools from captured images of machined parts using image texture features. Proc Inst Mech Eng Pt B: J Eng Manuf 228:203–214CrossRefGoogle Scholar
  9. 9.
    Dutta S, Pal SK, Sen R (2016) On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precis Eng 43:34–42CrossRefGoogle Scholar
  10. 10.
    Hsieh TH, Jywe WY, Huang HL, Chen SL (2011) Development of a laser-based measurement system for evaluation of the scraping workpiece quality. Opt Lasers Eng 49:1045–1053CrossRefGoogle Scholar
  11. 11.
    Tsai D-M, Hsiao B (2001) Automatic surface inspection using wavelet reconstruction. Pattern Recogn 34:1285–1305CrossRefGoogle Scholar
  12. 12.
    O. N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66CrossRefGoogle Scholar
  13. 13.
    Liao P-S, Chen T-S, Chung P-C (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17:713–727Google Scholar
  14. 14.
    Haar A (1910) Zur Theorie der orthogonalen Funktionensysteme. Math Ann 69:331–371MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li L, Xu H-H, Chang C-C, Ma Y-Y (2011) A novel image watermarking in redistributed invariant wavelet domain. J Syst Softw 84:923–929CrossRefGoogle Scholar
  16. 16.
    Trier OD, Jain AK (1995) Goal-directed evaluation of binarization methods. IEEE Trans Pattern Anal Mach Intell 17:1191–1201CrossRefGoogle Scholar
  17. 17.
    King R, Scraping technique training notes (2008) King-Way Machine Consultants, Inc.Google Scholar
  18. 18.
    Prabhu S, Karthik Saran S, Majumder D, Siva Teja PV (2015) A review on applications of image processing in inspection of cutting tool surfaces. Appl Mech Mater 766-767:635–642CrossRefGoogle Scholar
  19. 19.
    Chethan YD, Ravindra HV, Prashanth N, Gowda YTK, Gowda T (2015) Machine vision for correlating tool status and machined surface in turning nickel-base super alloy. In: 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp. 48-53.Google Scholar
  20. 20.
    Bhat NN, Dutta S, Vashisth T, Pal S, Pal SK, Sen R (2016) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83:1487–1502CrossRefGoogle Scholar
  21. 21.
    Teti R (2002) Machining of composite materials. CIRP Ann Manuf Technol 51:611–634CrossRefGoogle Scholar
  22. 22.
    Weckenmann A, Nalbantic K (2003) Precision measurement of cutting tools with two matched optical 3D-sensors. CIRP Ann Manuf Technol 52:443–446CrossRefGoogle Scholar
  23. 23.
    Castejón M, Alegre E, Barreiro J, Hernández LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tools Manuf 47:1847–1853CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Automation Engineering/Smart Machinery and Intelligent Manufacturing Research CenterNational Formosa UniversityYunlinRepublic of China
  2. 2.Department of Engineering ScienceNational Cheng Kung UniversityTainanRepublic of China

Personalised recommendations