Skip to main content
SpringerLink
Log in

Statistical quality control through process self-induced vibration spectrum analysis

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The first part of this study (Carnero et al., Mechanical Systems and Signal Processing, 24:1138–1160, 2010) analysed the influence of the process variables and work cycles on the quality of the bearings manufactured in an automotive bearing plant. The study was focused on the analysis of the overall vibration reading produced by the contact between the tool and the part. An analysis of variance was conducted on the overall vibration readings, which reflected that high-frequency vibration displacements are sensitive to process setup variables as well as the quality of products manufactured. Nevertheless, it was also observed that overall vibration values are not sufficient to analyse the relationship between the mechanical behaviour (vibration) and final quality obtained from high-precision machining processes. In this article, therefore, a new study is conducted based on spectral vibration measurements. A new experiment has been designed taking as input variables the diameter and the rotating speed of the tool. The selection criterion is based on the strong influence of these two variables on high-frequency vibration displacement and quality of parts (chattering, measured in terms of Lob A and Lob B). Two identical grinding machine tools were used during the experimental phase. Output variables are high-frequency displacements and high- and low-frequency chattering. The statistical analysis used in the new experiments determines spectral bands of the process in which vibrations induced by tool–part contact relates to development of lobes in the part to be identified. The study allows identification of vibration bands that are suitable for control in order to guarantee quality of the parts produced. To achieve that goal, the concept of spectral identity of the processes has been introduced to incorporate vibration induced by the process itself in the spectrums and to differentiate that process vibration from other mechanical vibration sources.

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. Tumer IY, Wood KL, Busch-Vishniac IJ (2000) Monitoring of signals from manufacturing processes using the Karhunen–Loeve transform. Mechanical Systems and Signal Processing 14(6):1011–1026

    Article  Google Scholar 

  2. Hahn RS (1995) Survey on Precision Grinding for improved product quality. Proceedings of the International Machine Tool Design and Research Conference, 3–16

  3. Brown R (1996) Profile. Brüel & Kraer 4(3):10–12

    Google Scholar 

  4. Harris TA (1984) Rolling Bearing analysis

  5. Cho DW, Eman KF (1988) Pattern recognition for on-line chatter detection. Mechanical Systems and Signal Processing 2(3):279–290

    Article  Google Scholar 

  6. Ruxu D, Elbestawi MA, Ullagaddi BC (1992) Chatter detection in milling based on the probability distribution of cutting force signal. Mechanical Systems and Signal Processing 6(4):345–362

    Article  Google Scholar 

  7. Gradišek J, Baus A, Govekar E, Klocke F, Grabec I (2003) Automatic chatter detection in grinding. International Journal of Machine Tools and Manufacture 43(14):1397–1403

    Article  Google Scholar 

  8. Cardi AA, Firpi HA, Bement MT, Liang SY (2008) Workpiece dynamic analysis and prediction during chatter of turning process. Mechanical Systems and Signal Processing 22(6):1481–1494

    Article  Google Scholar 

  9. Kuljanic E, Totis G, Sortino M (2009) Development of an intelligent multisensor chatter detection system in milling. Mechanical Systems and Signal Processing 23(5):1704–1718

    Article  Google Scholar 

  10. Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robotics and Computer-Integrated Manufacturing 25(6):989–998

    Article  Google Scholar 

  11. Yao Z, Mei D, Chen Z (2010) On-line chatter detection and identification based on wavelet and support vector machine. J Mater Process Technol 210(5):713–719

    Article  Google Scholar 

  12. Shiraishi M, Kume E, Hoshi T (1988) Suppression of machine-tool chatter by state feedback control. CIRP Annals—Manufacturing Technology 37(1):369–372

    Article  Google Scholar 

  13. Tarng YS, Kao JY, Lee EC (2000) Chatter suppression in turning operations with a tuned vibration absorber. J Mater Process Technol 105(1–2):55–60

    Article  Google Scholar 

  14. Inasaki I, Karpuschewski B, Lee HS (2001) Grinding chatter—origin and suppression. CIRP Annals—Manufacturing Technology 50(2):515–534

    Article  Google Scholar 

  15. Govekar E, Baus A, Gradišek J, Klocke F, Grabec IA (2002) New method for chatter detection in grinding. CIRP Annals—Manufacturing Technology 51(1):267–270

    Article  Google Scholar 

  16. Ema S, Marui E (2003) Theoretical analysis on chatter vibration in drilling and its suppression. J Mater Process Technol 138(1–3):572–578

    Article  Google Scholar 

  17. Zhongqun L, Qiang L (2008) Solution and analysis of chatter stability for end milling in the time-domain. Chin J Aeronaut 21(2):169–178

    Article  Google Scholar 

  18. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43(1–2):40–51

    Article  Google Scholar 

  19. Thomas Y, Beauchamp A, Youssef Y, Masounave J, (1996) Effect of tool vibrations on surface roughness during lathe dry turning process. Computers & Industrial Engineering. 18th International Conference on Computers and Industrial Engineering, 31(34):637–644

  20. Brinksmeier E, Heinzel C, Meyer L (2005) Development and application of a wheel based process monitoring system in grinding. CIRP Annals—Manufacturing Technology 54(1):301–304

    Article  Google Scholar 

  21. Vallejo A, Ruben MM, Hugo ES (2009) Surface roughness modelling in peripheral milling processes. Transactions of the North American Manufacturing Research Institution of SME 37:25–32

    Google Scholar 

  22. Salgado DR, Cambero I, Marcelo A, Alonso FJ (2009) Surface roughness prediction based on the correlation between surface roughness and cutting vibrations in dry turning with TiN-coated carbide Tools. Proceedings of the Institution of Mechanical Engineers, Part B. Journal of Engineering Manufacture 223(9):1193–1205

    Article  Google Scholar 

  23. Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205(1–3):439–450

    Article  Google Scholar 

  24. Lu C, Costes JP (2008) Surface profile prediction and analysis applied to turning process. Int J Mach Mach Mater 4(2–3):158–180

    Google Scholar 

  25. Rahnama R, Sajjadi M, Park SS (2009) Chatter suppression in micro end milling with process damping. J Mater Process Technol 209(17):5766–5776

    Article  Google Scholar 

  26. Tobias A (1961) Machine tool vibration research. International Journal of Machine Tool Design and Research 1(1–2):1–14

    Article  Google Scholar 

  27. Rao BKN (1996) Handbook of condition monitoring. Elsevier, Oxford (UK)

    Google Scholar 

  28. Zeng Y, Forssberg E (1994) Application of vibration signal measurement for monitoring grinding parameters. Mechanical Systems and Signal Processing 8(6):703–713

    Article  Google Scholar 

  29. Dalpiaz G, Rivola A (1997) Condition monitoring and diagnostics in automatic machines: comparison of vibration analysis techniques. Mechanical Systems and Signal Processing 11(1):53–73

    Article  Google Scholar 

  30. Abouelatta OB, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118(1–3):269–277

    Article  Google Scholar 

  31. Wong MLD, Jack LB, Nandi AK (2006) Modified self-organising map for automated novelty detection applied to vibration signal monitoring. Mechanical Systems and Signal Processing 20(3):593–610

    Article  Google Scholar 

  32. Choudhury SK, Goudimenko NN, Kudinov VA (1997) On-line control of machine tool vibration in turning. International Journal of Machine Tools and Manufacture 37(6):801–811

    Article  Google Scholar 

  33. Chern GL, Chang YC (2006) Using two-dimensional vibration cutting for micro-milling. International Journal of Machine Grinding wheels & Manufacture 46:659–666

    Article  Google Scholar 

  34. Devillez A, Dudzinski D (2007) Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers. Mechanical Systems and Signal Processing 21(1):441–456

    Article  Google Scholar 

  35. Bisu CF, Darnis P, Gérard A, K'Nevez JY (2009) Displacements analysis of self-excited vibrations in turning. Int J Adv Manuf Technol 44(1–2):1–16

    Article  Google Scholar 

  36. Snoeys R, Brown D (1969) Dominating parameters in grinding wheel, and workpiece regenerative chatter. 10th Int. Machine Tool Des. Res. Conference, 325–348

  37. Biera J, Nieto FJ, Iturriza I, Viñolas J, Goikoetxea P (1994) X Congreso de Investigación. Diseño y Utilización de Máquinas-Herramienta, Guipúzcoa (Spain), pp 1–17

    Google Scholar 

  38. Biera J, Nieto FJ, Viñolas J, Goikoetxea P (1994) XI National Congress of Mechanical Engineering, Valencia (Spain), 1–11

  39. Carnero MaC, Gónzalez-Palma R, Almorza D, Mayorga P, López-Escobar C (2010) Statistical quality control through overall vibration analysis. Mechanical Systems and Signal Processing 24:1138–1160

    Article  Google Scholar 

  40. Bothe KR (1991) World class quality. Using design of experiments to make it happen. Amacom. American Management Association

Download references

Author information

Authors and Affiliations

  1. Aluminum Company of America (ALCOA), Calle Pedro Teixeira no. 8, 28020, Madrid, Spain

    Carlos López-Escobar

  2. Technical School of Industrial Engineering, University of Cádiz, Calle Chile 1, 11002, Cádiz, Spain

    Rafael González-Palma

  3. School of Work Sciences, University of Cádiz, Calle Duque de Nájera 6, 11002, Cádiz, Spain

    David Almorza

  4. Electrical Technology Institute (ITE), Avenida de Juan de la Cierva 24, Parque Tecnológico de Valencia, 46980, Valencia, Spain

    Pedro Mayorga

  5. Technical School of Industrial Engineering, University of Castilla-La Mancha, Avda. Camilo José Cela s/n, 13071, Ciudad Real, Spain

    María Carmen Carnero

Authors
  1. Carlos López-Escobar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael González-Palma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David Almorza
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pedro Mayorga
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. María Carmen Carnero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to María Carmen Carnero.

Rights and permissions

Reprints and permissions

About this article

Cite this article

López-Escobar, C., González-Palma, R., Almorza, D. et al. Statistical quality control through process self-induced vibration spectrum analysis. Int J Adv Manuf Technol 58, 1243–1259 (2012). https://doi.org/10.1007/s00170-011-3462-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3462-8

Keywords

Search

Navigation