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Pattern recognition in audible sound energy emissions of AISI 52100 hardened steel turning: a MFCC-based approach

  • Edielson P. FrigieriEmail author
  • Tarcisio G. Brito
  • Carlos A. Ynoguti
  • Anderson P. Paiva
  • João R. Ferreira
  • Pedro P. Balestrassi
ORIGINAL ARTICLE

Abstract

The main objective in machining processes is to produce a high-quality surface finish which, however, can be measured only at the end of the machining cycle. A more preferable method would be to monitor the quality during the cycle, what result a real-time, low-cost, and accurate monitoring method that can dynamically adjust the machining parameters and keep the target surface finish. Motivated by this premise, results of investigation on the relationship between emitted sound signal and surface finish during turning process are reported in this paper. Through experiments with AISI 52100 hardened steel, this work shows that such a correlation does exist presenting strong evidences that Mel-Frequency Cepstral Coefficients, extracted from sound energy, can detect different surface roughness levels, what makes it a promising feature for real-time process quality monitoring methods.

Keywords

Sound Machining Monitoring Mel-frequency cesptral coefficients 

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References

  1. 1.
    Özel T, Karpat Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. Int J Mach Tools Manuf 45(4–5):467–479, 4. doi: 10.1016/j.ijmachtools.2004.09.007 CrossRefGoogle Scholar
  2. 2.
    Teti R, Jemielniak K, ODonnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59(2):717–739, 1. doi: 10.1016/j.cirp.2010.05.010 CrossRefGoogle Scholar
  3. 3.
    Bernhard S (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16(4):487–546, 7. doi: 10.1006/mssp.2001.1460 CrossRefGoogle Scholar
  4. 4.
    Dimla DE Sr., Lister PM (2000) On-line metal cutting tool condition monitoring. i : force and vibration analyses. Int J Mach Tools Manuf 40:739–768CrossRefGoogle Scholar
  5. 5.
    Xiqing Mu, Chuangwen Xu (2009) Tool wear monitoring of acoustic emission signals from milling processes. In: 2009 First international workshop on education technology and computer science, pp 431–435. IEEE. doi: 10.1109/ETCS.2009.105
  6. 6.
    Kasban H, Zahran O, Arafa H, El-Kordy M, Elaraby SMS, Abd El-Samie FE (2011) Welding defect detection from radiography images with a cepstral approach. NDT & E International 44(2):226–231, 3. doi: 10.1016/j.ndteint.2010.10.005 CrossRefGoogle Scholar
  7. 7.
    Chen B, Chen X, Li B, He Z, Cao H, Cai G (2011) Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mech Syst Signal Process 25(7):2526–2537, 10. doi: 10.1016/j.ymssp.2011.03.001 CrossRefGoogle Scholar
  8. 8.
    Rubio EM, Teti R (2010) Process monitoring systems for machining using audible sound energy sensors. In: Aized T (ed) Future Manufacturing Systems, pp 217–235. Sciyo. doi:  10.5772/55601, (to appear in print)
  9. 9.
    Mannan MA, Kassim AA, Ma J (2000) Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recogn Lett 21(11):969–979, 10. doi: 10.1016/S0167-8655(00)00050-7 CrossRefzbMATHGoogle Scholar
  10. 10.
    Ai CS, Sun YJ, He GW, Ze XB, Li W, Mao K (2011) The milling tool wear monitoring using the acoustic spectrum. Int J Adv Manuf Technol 61(5–8):457–463, 11. doi: 10.1007/s00170-011-3738-z Google Scholar
  11. 11.
    Emerson Raja J, Lim WS, Venkataseshaiah C (2012) Emitted sound analysis for tool flank wear monitoring using hilbert huang transform. International Journal of Computer and Electrical Engineering 4(2). doi: 10.7763/IJCEE.2012.V4.460
  12. 12.
    Boutros T, Liang M (2011) Detection and diagnosis of bearing and cutting tool faults using hidden markov models. Mech Syst Signal Process 25(6):2102–2124, 8. doi: 10.1016/j.ymssp.2011.01.013 CrossRefGoogle Scholar
  13. 13.
    Robben L, Rahman S, Buhl JC, Denkena B, Konopatzki B (2010) Airborne sound emission as a process monitoring tool in the cut-off grinding of concrete. Appl Acoust 71(1):52–60. doi: 10.1016/j.apacoust.2009.07.004 CrossRefGoogle Scholar
  14. 14.
    Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47(14):2140–2152. doi: 10.1016/j.ijmachtools.2007.04.013 CrossRefGoogle Scholar
  15. 15.
    Lu M-C, Wan B-S (2012) Study of high-frequency sound signals for tool wear monitoring in micromilling. Int J Adv Manuf Technol:1785–1792, 8. doi: 10.1007/s00170-012-4458-8
  16. 16.
    Downey J, OLeary P, Raghavendra R (2014) Comparison and analysis of audible sound energy emissions during single point machining of hsts with pvd ticn cutter insert across full tool life. Wear 313(1–2):53–62, 5. doi: 10.1016/j.wear.2014.02.004 CrossRefGoogle Scholar
  17. 17.
    Lee SS, Chen JC (2003) On-line surface roughness recognition system using artificial neural networks system in turning operations. Int J Adv Manuf Technol 22(7–8):498–509, 11. doi: 10.1007/s00170-002-1511-z CrossRefGoogle Scholar
  18. 18.
    Khorasani AM, Yazdi MRS (2015) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ann) in milling operation. Int J Adv Manuf Technol:10. doi: 10.1007/s00170-015-7922-4
  19. 19.
    Koolagudi SG, Rastogi D, Sreenivasa Rao K (2012) Identification of language using mel-frequency cepstral coefficients (mfcc). Procedia Engineering 38:3391–3398, 1. doi: 10.1016/j.proeng.2012.06.392 CrossRefGoogle Scholar
  20. 20.
    Junqin W, Junjun Y (2011) An improved arithmetic of mfcc in speech recognition system. In: 2011 International conference on electronics communications and control ICECC, pp 719–722. IEEE, doi: 10.1109/ICECC.2011.6066676, (to appear in print)
  21. 21.
    Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510, 10. doi: 10.1016/j.ymssp.2005.09.012 CrossRefGoogle Scholar
  22. 22.
    Wang C-C, Kang Y (2012) Feature extraction techniques of non-stationary signals for fault diagnosis in machinery systems. Journal of Signal and Information Processing 03(01):16–25. doi: 10.4236/jsip.2012.31002 CrossRefGoogle Scholar
  23. 23.
    Cohen L (1994) Time Frequency Analysis: Theory and Applications, 1st edn. Prentice HallGoogle Scholar
  24. 24.
    Marinescu I, Axinte D (2009) A time-frequency acoustic emission-based monitoring technique to identify workpiece surface malfunctions in milling with multiple teeth cutting simultaneously. Int J Mach Tools Manuf 49(1):53–65. doi: 10.1016/j.ijmachtools.2008.08.002 CrossRefGoogle Scholar
  25. 25.
    Picone JW (1993) Signal modeling techniques in speech recognition. Proc IEEE 81(9):1215–1247. doi: 10.1109/5.237532 CrossRefGoogle Scholar
  26. 26.
    Davis S, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentencesGoogle Scholar
  27. 27.
    Shantha Selva Kumari R, Selva Nidhyananthan S, Anand G (2012) Fused mel feature sets based text-independent speaker identification using gaussian mixture model. Procedia Engineering 30:319–326, 1. doi: 10.1016/j.proeng.2012.01.867 CrossRefGoogle Scholar
  28. 28.
    Dhanalakshmi P, Palanivel S, Ramalingam V (2011) Classification of audio signals using aann and gmm. Appl Soft Comput 11(1):716–723, 1. doi: 10.1016/j.asoc.2009.12.033 CrossRefGoogle Scholar
  29. 29.
    Fahmy MMM (2010) Palmprint recognition based on mel frequency cepstral coefficients feature extraction. Ain Shams Engineering Journal 1(1):39–47, 9. doi: 10.1016/j.asej.2010.09.005 CrossRefGoogle Scholar
  30. 30.
    Dannenberg R (2013) Audacity. http://audacity.sourceforge.net/
  31. 31.
    Paiva AP, Campos PH, Ferreira JR, Lopes LGD, Paiva EJ, Balestrassi PP (2012) A multivariate robust parameter design approach for optimization of aisi 52100 hardened steel turning with wiper mixed ceramic tool. Int J Refract Met Hard Mater 30(1):152–163, 1. doi: 10.1016/j.ijrmhm.2011.08.001 CrossRefGoogle Scholar
  32. 32.
    Montgomery DC (2013) Design and Analysis of Experiments, 8edn. WileyGoogle Scholar
  33. 33.
    Tekner Z, Yeşlyurt S (2004) Investigation of the cutting parameters depending on process sound during turning of aisi 304 austenitic stainless steel. Mater Des 25(6):507–513, 9. doi: 10.1016/j.matdes.2003.12.011. http://linkinghub.elsevier.com/retrieve/pii/S0261306903002632 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Edielson P. Frigieri
    • 1
    Email author
  • Tarcisio G. Brito
    • 1
  • Carlos A. Ynoguti
    • 2
  • Anderson P. Paiva
    • 1
  • João R. Ferreira
    • 1
  • Pedro P. Balestrassi
    • 1
  1. 1.Industrial Engineering InstituteFederal University of ItajubaItajubaBrazil
  2. 2.Electrical Engineering DepartmentNational Institute of TelecommunicationSanta Rita do SapucaiBrazil

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