Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

  • Milad Elgargni
  • Amin Al-Habaibeh
  • Ahmad Lotfi


The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The suggested technique is based on ‘learning from experience’ concept. The application of principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks is investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using suitable analysis and image processing algorithms. The capabilities of PCA and DWT combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms for these suggested self-learning techniques.


Image processing PCA Neural network Tool recognition Self-learning Manufacturing processes 


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  1. 1.
    Al-Habaibeh A, Liu G, Gindy N (2002) Sensor fusion for an integrated process and machine condition monitoring system. 15th Triennial World Congress of the International Federation of Automatic Control. Barcelona, SpainGoogle Scholar
  2. 2.
    Al-Habaibeh A, Gindy N (2001) Self-learning algorithm for automated design of condition monitoring systems for milling operations. Int J Adv Manuf Technol 18(6):448–459CrossRefGoogle Scholar
  3. 3.
    Al-Habaibeh A, Zorriassatine F, Gindy N (2002) Comprehensive experimental evaluation of a systematic approach for cost effective and rapid design of condition monitoring systems using Taguchi’s method. J Mater Process Technol 124(3):372–383, ISSN 0924–0136CrossRefGoogle Scholar
  4. 4.
    Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2014) A review of sensor system and application in milling process for tool condition monitoring. Res J Appl Sci Eng Technol 7(10):2083–2097Google Scholar
  5. 5.
    Ogedengbe TI, Heinemann R, Hinduja S (2011) Feasibility of tool condition monitoring on micro-milling using current signals. Assumption U J Tech 14(3):61–172Google Scholar
  6. 6.
    Zuperl U, Cus F, Balic J (2011) Intelligent cutting tool condition monitoring in milling. J Achievements MaterManuf Eng 49(2):477–486Google Scholar
  7. 7.
    Bisu C, Gerard A, Zapciu M, Cahuc O (2011) The milling process monitoring using 3D envelope method. Adv Mater Res 423:77–88CrossRefGoogle Scholar
  8. 8.
    Girardin F, Remond D, Rigal JF (2010) A new method for detecting tool wear and breakage in milling. Int J Mater Form 3(1):463–466CrossRefGoogle Scholar
  9. 9.
    Kious M, Ouahabi A, Boudraa M, Serra R, Cheknane A (2010) Detection process approach of tool wear in high speed milling. Measurement 43:1439–1446CrossRefGoogle Scholar
  10. 10.
    Figliola RS, Beasley DE (2000) Theory and design for mechanical measurements, 3rd edn. John Wiley and Sons, Inc., New YorkGoogle Scholar
  11. 11.
    Chuangwen X, Zhe L, Wencui L (2009) A frequency band energy analysis of vibration signals for tool condition monitoring. Proceeding of International Conference on Measuring Technology and Mechatronics AutomationGoogle Scholar
  12. 12.
    Kalvoda T, Hwang YR (2010) A cutter tool monitoring in machining process using Hilbert. Int J Mach Tool Manuf 50:495–501CrossRefGoogle Scholar
  13. 13.
    Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180:328–335CrossRefGoogle Scholar
  14. 14.
    Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209(10):4903–4914CrossRefGoogle Scholar
  15. 15.
    Atli AV, Urhan O, Ertürk S, Sönmez M (2006) A computer vision-based fast approach to drilling tool condition monitoring. Proc Inst Mech Eng B J Eng Manuf 220(9):1409–1415CrossRefGoogle Scholar
  16. 16.
    Shahabi HH, Low TH, Ratnam MM (2009) Notch wear detection in cutting tools using gradient approach and polynomial fitting. Int J 40:1057–1066Google Scholar
  17. 17.
    Bagavathiappan S, Lahiri BB, Saravanan T, Philip J, Jayakumar T (2013) Infrared physics & technology infrared thermography for condition monitoring—a review. Infrared Phys Technol 60:35–55CrossRefGoogle Scholar
  18. 18.
    Al-Habaibeh A, Parkin R (2003) An autonomous low-cost infrared system for the on-line monitoring of manufacturing processes using novelty detection. Int J Adv Manuf Technol 22(3–4).Springer-Verlag London Ltd, ISSN: 0268–3768Google Scholar
  19. 19.
    Al-Habaibeh A, Shi F, Brown N, Kerr D, Jackson M, Parkin RM (2004) A novel approach for quality control system using sensor fusion of infrared and visual image processing for laser sealing of food containers. Meas Sci Technol 15(10):1995–2000CrossRefGoogle Scholar
  20. 20.
    Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes—a review article. Measurement 58:73–86CrossRefGoogle Scholar
  21. 21.
    Colom-Palero RJ, Gadea-Girones R, Ballester-Merelo FJ, Martínez-Peiro M (2004) Flexible architecture for the implementation of the two-dimensional discrete wavelet transform (2D-DWT) oriented to FPGA devices. Microprocess Microsyst 28(9):509–518CrossRefGoogle Scholar
  22. 22.
    Akansu AN, Serdijn WA, Selesnick IW (2010) Emerging applications of wavelets: a review. Phys Commun 3(1):1–18CrossRefGoogle Scholar
  23. 23.
    Asadi S, Rao CDVS, Saikrishna V (2010) A comparative study of face recognition with principal component analysis and cross-correlation technique. Int J Comput Appl 10(8):17–21Google Scholar
  24. 24.
    Gan J, Zhou D, Li C (2005) A method for improved PCA in face recognition. Int J Inf Technol 11(11):79–85Google Scholar
  25. 25.
    Panda SS, Deepthi G, Anisha V (2011) Face recognition using PCA and feed forward neural networks. Int J Comput Sci Telecommun 2(8):79–82Google Scholar
  26. 26.
    Kaymak S (2003) Face detection, recognition and reconstruction using eigenfacesGoogle Scholar
  27. 27.
    Park H, Schoepflin T, Kim Y (2001) Active contour model with gradient directional information: directional snake. Trans Circuits Syst 11(2):252–256Google Scholar
  28. 28.
    Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322CrossRefGoogle Scholar
  29. 29.
    Chaovalit P, Gangopadhyay A, Karabatis G, Chen Z (2011) Discrete wavelet transform-based time series analysis and mining. ACM Comput Surv 43(2).doi:10.1145/1883612.1883613
  30. 30.
    Galiano V, Lopez-Granado O, Malumbres MP, Drummond LA, Migallon H (2013) GPU-based 3D lower tree wavelet video encoder. EURASIP J Adv Signal Process 1:1–13Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Advanced Design and Manufacturing Engineering Centre, School of Architecture, Design and the Built EnvironmentNottingham Trent UniversityNottinghamUK
  2. 2.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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