Abstract
Due to its relatively high gravity material removal, the thin-walled part machining would go through a complex process, from stable to unstable and/or reverse repeatedly. As a result, the monitored signals generally exhibit full-oscillatory behaviors, which require that the chatter indicators should meet the dynamic conditions. However, the conventional indicators, including time domain indicators and time-frequency domain indicators, could only capture the state mutation point in the continuous process. In this paper, a novel chatter indicator, Q-factors, is proposed for chatter detection. The relationship between Q-factor and signal oscillatory behavior is illustrated from the perspective of signal’s frequency characteristics and tool-workpiece system’s response. Chatter indicator’s identification ability for thin-walled part flank and mirror milling is analyzed, i.e., its ability to express characteristics of machining state, sensibility to change machining state, and its chatter-related information inclusion. It can be indicated that as a multi-dimensional indicator, Q-factor can be used to identify chatter-related signal component and quantify the level of chatter simultaneously. The value of Q-factor exhibits obvious difference between stable state and chatter state. The obvious mutation at the place where the machining state changes will supply more useful and effective information for the following chatter prediction and suppression before the chatter is completely developed.
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References
Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376
Lamraoui M, Thomas M, Badaoui ME, Girardin F (2014) Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mech Syst Signal Process 44:72–85
Hu CQ, Smith WA, Randall RB, Peng ZX (2016) Development of a gear vibration indicator and its application in gear wear monitoring. Mech Syst Signal Process 76-77:319–336
Davies MA, Balachandran B (2000) Impact dynamics in milling of thin wall structures. Nonlinear Dyn 22:375–392
Lai GJ, Chang JY (1995) Stability analysis of chatter vibration for a thin-wall cylindrical workpiece. Int J Mach Tools Manuf 35:431–444
Yan ZH, Liu ZB, Wang XB, Liu B, Luo ZW, Wang DQ (2016) Stability prediction of thin-walled workpiece made of Al7075 in milling based on shifted Chebyshev polynomials. Int J Adv Manuf Technol 87:1–10
Campa FJ, Lacalle LNLD, Celaya A (2011) Chatter avoidance in the milling of thin floors with bull-nose end mills: model and stability diagrams. Int J Mach Tools Manuf 51:43–53
Atlar S, Budak E, Özgüven HN (2008) Modeling part dynamics and chatter stability in machining considering material removal
Luo M, Zhang DH, Wu BH, Tang M (2011) Modeling and analysis effects of material removal on machining dynamics in milling of thin-walled workpiece. Adv Mater 223:671–678
Alan S, Budak E, Özgüven HN (2010) Analytical prediction of part dynamics for machining stability analysis. Int J Automat Technol 4
Song QH, Liu ZQ, Wan Y, Ju GG, Shi JH (2015) Application of Sherman–Morrison–Woodbury formulas in instantaneous dynamic of peripheral milling for thin-walled component. Int J Mech Sci 96–97:79–90
Wang L, Liang M (2009) Chatter detection based on probability distribution of wavelet modulus maxima. Robot Comput Integr Manuf 25:989–998
Sick B (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 16:487–546
Dong J, Subrahmanyam KVR, Wong YS, Hong GS, Mohanty AR (2006) Bayesian-inference-based neural networks for tool wear estimation. Int J Adv Manuf Technol 30:797–807
Salgado DR, Alonso FJ (2006) Tool wear detection in turning operations using singular spectrum analysis. J Mater Process Technol 171:451–458
Kim HY, Ahn JH (2002) Chip disposal state monitoring in drilling using neural network based spindle motor power sensing. Int J Mach Tools Manuf 42:1113–1119
Zhu K, Wong YS, Hong GS (2009) Multi-category micro-milling tool wear monitoring with continuous hidden Markov models. Mech Syst Signal Process 23:547–560
Binsaeid S, Asfour S, Cho S, Onar A (2009) Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J Mater Process Technol 209:4728–4738
Dron JP, Bolaers F, Rasolofondraibe L (2004) Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. J Sound Vib 270:61–73
Fu Y, Zhang Y, Zhou HM, Li DQ, Liu HQ, Qiao HY, Wang XQ (2016) Timely online chatter detection in end milling process. Mech Syst Signal Process 75:668–688
Al-Ghamd AM, Mba DA (2006) Comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20:1537–1571
Zhang H, Chen XF, Du ZH, Yan RQ (2016) Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis. Mech Syst Signal Process 80:349–376
Al-Habaibeh A, Gindy N (2000) A new approach for systematic design of condition monitoring systems for milling processes. J Mater Process Technol 107:243–251
Choi T, Shin YC (2003) On-line chatter detection using wavelet-based parameter estimation. J Manuf Sci Eng 125:21–28
Bhattacharyya P, Sengupta D, Mukhopadhyay S (2007) Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques. Mech Syst Signal Process 21:2665–2683
Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739
Lamraoui M, Barakat M, Thomas M, Badaoui ME (2015) Chatter detection in milling machines by neural network classification and feature selection. J Vib Control 21:1251–1266
Ren JB, Sun GZ, Chen B, Luo M (2015) Multi-scale permutation entropy based on-line milling chatter detection method. J Mech Eng 51:206
Nair U, Krishna BM, Namboothiri VNN, Nampoori VPN (2010) Permutation entropy based real-time chatter detection using audio signal in turning process. Int J Adv Manuf Technol 46:61–68
Marinescu I, Axinte DA (2008) A critical analysis of effectiveness of acoustic emission signals to detect tool and workpiece malfunctions in milling operations. Int J Mach Tools Manuf 48:1148–1160
Jemielniak K (2000) Some aspects of AE application in tool condition monitoring. Ultrasonics 38:604–608
Maggioni M, Marzorati E, Grasso M, Colosimo BM (2014) In-process quality characterization of grinding processes: a sensor-fusion based approach. ASME Biennial Conference on Engineering Systems Design, Esda, pp 1–2
Cao H, Zhou K, Chen X (2015) Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. Int J Mach Tools Manuf 92:52–59
Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20:282–307
Chen BQ, Zhang ZS, Zi YY, He ZJ, Sun C (2013) Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery. Mech Syst Signal Process 40:1–37
Wang YX, Liang M (2011) An adaptive SK technique and its application for fault detection of rolling element bearings. Mech Syst Signal Process 25:1750–1764
Yang Y, YuDejie CJS (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib 294:269–277
Liu C, Zhu L, Ni C (2017) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol 91:3339–3348
Shao H, Shi X, Li L (2011) Power signal separation in milling process based on wavelet transform and independent component analysis. Int J Mach Tools Manuf 51:701–710
Plaza EG, López PJN (2017) Surface roughness monitoring by singular spectrum analysis of vibration signals. Mech Syst Signal Process 84:516–530
Hu CZ, Yang Q, Huang MY, Yan WJ (2017) Sparse component analysis-based under-determined blind source separation for bearing fault feature extraction in wind turbine gearbox. IET Renew Power Gener 11:330–337
Zhong ZM, Chen J, Zhong P, Wu JB (2006) Application of the blind source separation method to feature extraction of machine sound signals. Int J Mach Tools Manuf 28:855–862
Cai GG, Chen XF, He ZJ (2013) Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mech Syst Signal Process 41:34–53
Selesnick IW (2011) Resonance-based signal decomposition: a new sparsity-enabled signal analysis method. Signal Process 91:2793–2809
Shi JJ, Liang M (2016) Intelligent bearing fault signature extraction via iterative oscillatory behavior based signal decomposition (IOBSD). Expert Syst Appl 45:40–55
Wang HC, Chen J, Dong GM (2014) Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mech Syst Signal Process 48:103–119
Wang M, Gao L, Zheng Y (2014) Prediction of regenerative chatter in the high-speed vertical milling of thin-walled workpiece made of titanium alloy. Int J Adv Manuf Technol 72:707–716
Siebert WM (1986) Circuits, signals, and systems. MIT Press 86:21–134
Wang YQ, Bo QL, Liu HB, Lian M, Wang F, Zhang J (2017) Full-oscillatory components decomposition from noisy machining vibration signals by minimizing the Q-factor variation. Trans Inst Meas Control 39(9):1313–1328
Polito F, Petri A, Pontuale G, Dalton F (2010) Analysis of metal cutting acoustic emissions by time series models. Int J Adv Manuf Technol 48:897–903
Aghdam BH, Vahdati M, Sadeghi MH (2015) Vibration-based estimation of tool major flank wear in a turning process using ARMA models. Int J Adv Manuf Technol 76:1631–1642
Levinson N (1946) The Wiener (root mean square) error criterion in filter design and prediction. J Math Phys 25:261–278
Lan J, Lin B, Huang T, Xiao JL, Zhang XF, Fei JX (2017) Path planning for support heads in mirror-milling machining system. Int J Adv Manuf Technol 91:617–628
Funding
This work is supported by National Basic Research Program Funding Agency of China (Grant No. 2014CB046604), by the Fundamental Research Funds for the Central Universities (Grant No. DUT17JC16), and by Open Research Fund of Key Laboratory of High Performance Complex Manufacturing, Central South University (Grant No. Kfkt2016-05).
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Liu, H., Bo, Q., Zhang, H. et al. Analysis of Q-factor’s identification ability for thin-walled part flank and mirror milling chatter. Int J Adv Manuf Technol 99, 1673–1686 (2018). https://doi.org/10.1007/s00170-018-2580-y
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DOI: https://doi.org/10.1007/s00170-018-2580-y