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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


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.

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  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, Spain

  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):448–459

    Article  Google Scholar 

  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 0924–0136

    Article  Google Scholar 

  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):2083–2097

    Google Scholar 

  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):61–172

  6. Zuperl U, Cus F, Balic J (2011) Intelligent cutting tool condition monitoring in milling. J Achievements MaterManuf Eng 49(2):477–486

  7. Bisu C, Gerard A, Zapciu M, Cahuc O (2011) The milling process monitoring using 3D envelope method. Adv Mater Res 423:77–88

    Article  Google Scholar 

  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):463–466

    Article  Google Scholar 

  9. Kious M, Ouahabi A, Boudraa M, Serra R, Cheknane A (2010) Detection process approach of tool wear in high speed milling. Measurement 43:1439–1446

    Article  Google Scholar 

  10. Figliola RS, Beasley DE (2000) Theory and design for mechanical measurements, 3rd edn. John Wiley and Sons, Inc., New York

    Google Scholar 

  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 Automation

  12. Kalvoda T, Hwang YR (2010) A cutter tool monitoring in machining process using Hilbert. Int J Mach Tool Manuf 50:495–501

    Article  Google Scholar 

  13. Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180:328–335

    Article  Google Scholar 

  14. Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209(10):4903–4914

    Article  Google Scholar 

  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):1409–1415

    Article  Google Scholar 

  16. Shahabi HH, Low TH, Ratnam MM (2009) Notch wear detection in cutting tools using gradient approach and polynomial fitting. Int J 40:1057–1066

    Google Scholar 

  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 60:35–55

    Article  Google Scholar 

  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: 0268–3768

  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):1995–2000

    Article  Google Scholar 

  20. Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes—a review article. Measurement 58:73–86

    Article  Google Scholar 

  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):509–518

    Article  Google Scholar 

  22. Akansu AN, Serdijn WA, Selesnick IW (2010) Emerging applications of wavelets: a review. Phys Commun 3(1):1–18

    Article  Google Scholar 

  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):17–21

    Google Scholar 

  24. Gan J, Zhou D, Li C (2005) A method for improved PCA in face recognition. Int J Inf Technol 11(11):79–85

    Google Scholar 

  25. Panda SS, Deepthi G, Anisha V (2011) Face recognition using PCA and feed forward neural networks. Int J Comput Sci Telecommun 2(8):79–82

    Google Scholar 

  26. Kaymak S (2003) Face detection, recognition and reconstruction using eigenfaces

  27. Park H, Schoepflin T, Kim Y (2001) Active contour model with gradient directional information: directional snake. Trans Circuits Syst 11(2):252–256

  28. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28:316–322

    Article  Google Scholar 

  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. 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 1:1–13

    Google Scholar 

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Correspondence to Amin Al-Habaibeh.

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Elgargni, M., Al-Habaibeh, A. & Lotfi, A. 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. Int J Adv Manuf Technol 77, 1965–1978 (2015).

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