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.
Similar content being viewed by others
References
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
Hahn RS (1995) Survey on Precision Grinding for improved product quality. Proceedings of the International Machine Tool Design and Research Conference, 3–16
Brown R (1996) Profile. Brüel & Kraer 4(3):10–12
Harris TA (1984) Rolling Bearing analysis
Cho DW, Eman KF (1988) Pattern recognition for on-line chatter detection. Mechanical Systems and Signal Processing 2(3):279–290
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
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
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
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
Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robotics and Computer-Integrated Manufacturing 25(6):989–998
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
Shiraishi M, Kume E, Hoshi T (1988) Suppression of machine-tool chatter by state feedback control. CIRP Annals—Manufacturing Technology 37(1):369–372
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
Inasaki I, Karpuschewski B, Lee HS (2001) Grinding chatter—origin and suppression. CIRP Annals—Manufacturing Technology 50(2):515–534
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
Ema S, Marui E (2003) Theoretical analysis on chatter vibration in drilling and its suppression. J Mater Process Technol 138(1–3):572–578
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
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
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
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
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
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
Lu C (2008) Study on prediction of surface quality in machining process. J Mater Process Technol 205(1–3):439–450
Lu C, Costes JP (2008) Surface profile prediction and analysis applied to turning process. Int J Mach Mach Mater 4(2–3):158–180
Rahnama R, Sajjadi M, Park SS (2009) Chatter suppression in micro end milling with process damping. J Mater Process Technol 209(17):5766–5776
Tobias A (1961) Machine tool vibration research. International Journal of Machine Tool Design and Research 1(1–2):1–14
Rao BKN (1996) Handbook of condition monitoring. Elsevier, Oxford (UK)
Zeng Y, Forssberg E (1994) Application of vibration signal measurement for monitoring grinding parameters. Mechanical Systems and Signal Processing 8(6):703–713
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
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
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
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
Chern GL, Chang YC (2006) Using two-dimensional vibration cutting for micro-milling. International Journal of Machine Grinding wheels & Manufacture 46:659–666
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
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
Snoeys R, Brown D (1969) Dominating parameters in grinding wheel, and workpiece regenerative chatter. 10th Int. Machine Tool Des. Res. Conference, 325–348
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
Biera J, Nieto FJ, Viñolas J, Goikoetxea P (1994) XI National Congress of Mechanical Engineering, Valencia (Spain), 1–11
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
Bothe KR (1991) World class quality. Using design of experiments to make it happen. Amacom. American Management Association
Author information
Authors and Affiliations
Corresponding author
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-011-3462-8