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Investigation of tool chatter using local mean decomposition and artificial neural network during turning of Al 6061

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Abstract

Al 6061 because of its high strength, lightweight and good corrosion resistance is being widely used in aerospace industry. Regenerative chatter is an unavoidable phenomenon often encountered during machining of this aluminum alloy. In the present work, a methodology has been proposed in order to extract tool chatter features during turning operation. Chatter signals generated during the turning of Al 6061 have been acquired using a microphone. The recorded signals have been processed using local mean decomposition. The decomposed signals have been analyzed using different statistical indicators considering Nakagami distribution approach for ascertaining the thresholds of chatter severity. Prediction models of most effective statistical indicator have been developed using artificial neural network to ascertain safe range of cutting parameters. From the analysis and validation experiments, it has been inferred that the obtained safe cutting zone is significant and capable of limiting the chatter.

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References

  • Albertelli P, Braghieri L, Torta M, Monno M (2019) Development of a generalized chatter detection methodology for variable speed machining. Mech Syst Signal Process 123:26–42

    Google Scholar 

  • Altintas Y, Automation M (2000) Metal cutting mechanics, machine tool vibrations, and CNC design. Manuf Automat 56–64

  • Altintas Y, Stepan G, Merdol D, Dombovari Z (2008) Chatter stability of milling in frequency and discrete time domain. CIRP J Manuf Sci Technol 1:35–44

    Google Scholar 

  • Arqub OAJNC (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl. 28:1591–1610

    Google Scholar 

  • Arqub OA, Al-Smadi MJSC (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Comput 24:01–22

    MATH  Google Scholar 

  • Arqub OA, Al-Smadi M, Momani S, Hayat TJSC (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191–7206

    MATH  Google Scholar 

  • Cao H, Lei Y, He Z (2013) Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int J Mach Tools Manuf 69:11–19

    Google Scholar 

  • 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

    Google Scholar 

  • Cao H, Yue Y, Chen X, Zhang X (2017a) Chatter detection in milling process based on synchrosqueezing transform of sound signals. Int J Adv Manuf Technol 89:2747–2755

    Google Scholar 

  • Cao H, Zhang X, Chen X (2017b) The concept and progress of intelligent spindles: a review. Int J Mach Tools Manuf 112:21–52

    Google Scholar 

  • Cao H, Yue Y, Chen X, Zhang X (2018) Chatter detection based on synchrosqueezing transform and statistical indicators in milling process. Int J Adv Manuf Technol 95:961–972

    Google Scholar 

  • Cardi AA, Firpi HA, Bement MT, Liang SY (2008) Workpiece dynamic analysis and prediction during chatter of turning process. Mech Syst Signal Process 22:1481–1494

    Google Scholar 

  • Cheng J, Zhang K, Yang Y (2012) An order tracking technique for the gear fault diagnosis using local mean decomposition method. Mech Mach Theory 55:67–76

    Google Scholar 

  • Choi T, Shin YC (2003) On-line chatter detection using wavelet-based parameter estimation. J Manuf Sci Eng 125:21–28

    Google Scholar 

  • Delio T, Tlusty J, Smith S (1992) Use of audio signals for chatter detection and control. J Eng Ind 114:146–157

    Google Scholar 

  • Fu Y, Zhang Y, Zhou H, Li D, Liu H, Qiao H, Wang X (2016) Timely online chatter detection in end milling process. Mech Syst Signal Process 75:668–688

    Google Scholar 

  • Gao B, Woo W, Dlay S (2011) Single-channel source separation using EMD-subband variable regularized sparse features. IEEE Trans Audio Speech Lang Process 19:961–976

    Google Scholar 

  • Heaton J (2008) Introduction to neural networks with Java. Heaton Research Inc

    Google Scholar 

  • Hossain MS, Ong ZC, Ismail Z, Noroozi S, Khoo SY (2017) Artificial neural networks for vibration based inverse parametric identifications: a review. Appl Soft Comput 52:203–219

    Google Scholar 

  • Huang NE (2014) Hilbert-Huang transform and its applications, vol 16. World Scientific

    MATH  Google Scholar 

  • Huang NE et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser Math Phys Eng Sci 454:903–995

    MathSciNet  MATH  Google Scholar 

  • Kakinuma Y, Sudo Y, Aoyama T (2011) Detection of chatter vibration in end milling applying disturbance observer. CIRP Ann 60:109–112

    Google Scholar 

  • Khraisheh M, Pezeshki C, Bayoumi A (1995) Time series based analysis for primary chatter in metal cutting. J Sound Vib 180:67–87

    Google Scholar 

  • Kohli A, Dixit U (2005) A neural-network-based methodology for the prediction of surface roughness in a turning process. Int J Adv Manuf Technol 25:118–129

    Google Scholar 

  • Kuljanic E, Sortino M, Totis G (2008) Multisensor approaches for chatter detection in milling. J Sound Vib 312:672–693

    Google Scholar 

  • Kumar S, Singh B (2019) Ascertaining of chatter stability using wavelet denoising and artificial neural network. Proc Inst Mech Eng C J Mech Eng Sci 233:39–62

    Google Scholar 

  • Lamraoui M, Thomas M, El Badaoui M (2014a) Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mech Syst Signal Process 44:177–198

    Google Scholar 

  • Lamraoui M, Thomas M, El Badaoui M, Girardin F (2014b) Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mech Syst Signal Process 44:72–85

    Google Scholar 

  • Li X, Mei D-Q, Chen Z-C (2011) Feature extraction of chatter for precision hole boring processing based on EMD and HHT. Guangxue Jingmi Gongcheng Opt Precision Eng 19:1291–1297

    Google Scholar 

  • Liu C, Zhu L, Ni C (2017a) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol 91:3339–3348

    Google Scholar 

  • Liu C, Zhu L, Ni C (2017b) The chatter identification in end milling based on combining EMD and WPD. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-017-0024-8

    Article  Google Scholar 

  • Maragos P, Kaiser JF, Quatieri TF (1993) On amplitude and frequency demodulation using energy operators. IEEE Trans Signal Process 41:1532–1550

    MATH  Google Scholar 

  • 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

    Google Scholar 

  • Marinescu I, Axinte DA (2011) An automated monitoring solution for avoiding an increased number of surface anomalies during milling of aerospace alloys. Int J Mach Tools Manuf 51:349–357

    Google Scholar 

  • Momani S, Abo-Hammour ZS, Alsmadi OMJAM, Sciences I (2016) Solution of inverse kinematics problem using genetic algorithms. Appl Math Inf Sci 10:225

    Google Scholar 

  • Nakagami N (1960) The m-Distribution, a general formula of intensity of rapid fading In: Hoffman WC (ed) Statistical methods in radio wave propagation: proc. of a symp. held June 18–20, 1958. Permagon Press

  • Natarajan C, Muthu S, Karuppuswamy P (2011) Prediction and analysis of surface roughness characteristics of a non-ferrous material using ANN in CNC turning. Int J Adv Manuf Technol 57:1043–1051

    Google Scholar 

  • Oh S-j, Lim C-o, Park B-c, Lee J-c, S-cJIJoFL S, Systems I (2019) Deep neural networks for maximum stress prediction in piping design. Int J Fuzzy Logic Intell Syst 19:140–146

    Google Scholar 

  • Pal SK, Chakraborty D (2005) Surface roughness prediction in turning using artificial neural network. Neural Comput Appl 14:319–324

    Google Scholar 

  • Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18:199–221

    Google Scholar 

  • Pérez-Canales D, Álvarez-Ramírez J, Jáuregui-Correa JC, Vela-Martínez L, Herrera-Ruiz G (2011) Identification of dynamic instabilities in machining process using the approximate entropy method. Int J Mach Tools Manuf 51:556–564

    Google Scholar 

  • Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376

    Google Scholar 

  • Rafal R, Pawel L, Krzysztof K, Bogdan K, Jerzy W (2015) Chatter identification methods on the basis of time series measured during titanium superalloy milling. Int J Mech Sci 99:196–207

    Google Scholar 

  • Ramos PM, Martins RC, Rapuano S, Daponte P (2009) Frequency and time—frequency domain analysis tools in measurement. In: Data modeling for metrology and testing in measurement science. Springer, pp 1–27

  • Schmitz TL (2003) Chatter recognition by a statistical evaluation of the synchronously sampled audio signal. J Sound Vib 262:721–730

    Google Scholar 

  • Shrivastava Y, Singh B (2018a) Estimation of stable cutting zone in turning based on empirical mode decomposition and statistical approach. J Braz Soc Mech Sci Eng 40:77

    Google Scholar 

  • Shrivastava Y, Singh B (2018b) Possible way to diminish the effect of chatter in CNC turning based on EMD and ANN approaches. Arab J Sci Eng 43:4571–4591

    Google Scholar 

  • Shrivastava Y, Singh B (2019) A comparative study of EMD and EEMD approaches for identifying chatter frequency in CNC turning. Eur J Mech A Solids 73:381–393

    Google Scholar 

  • Shrivastava Y, Singh B, Sharma A (2018) Identification of chatter in turning operation using WD and EMD. Mater Today Proc 5:23917–23926

    Google Scholar 

  • Siddhpura M, Paurobally R (2012) A review of chatter vibration research in turning. Int J Mach Tools Manuf 61:27–47

    Google Scholar 

  • Siddhpura M, Siddhpura A, Paurobally R (2017) Chatter stability prediction for a flexible tool-workpiece system in a turning process. Int J Adv Manuf Technol 1–16

  • Smith JS (2005) The local mean decomposition and its application to EEG perception data. J R Soc Interface 2:443–454

    Google Scholar 

  • Tobias S (1961) Machine tool vibration research. Int J Mach Tool Des Res 1:1–14

    Google Scholar 

  • Uekita M, Takaya Y (2017) Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain. Measurement 103:199–207

    Google Scholar 

  • Wan M, Feng J, Ma Y-C, Zhang W-H (2017) Identification of milling process damping using operational modal analysis. Int J Mach Tools Manuf 122:120–131

    Google Scholar 

  • Wang Y, He Z, Zi Y (2010) A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. J Vib Acoust 132:021010

    Google Scholar 

  • Wang T, Zhang M, Yu Q, Zhang H (2012) Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. J Appl Geophys 83:29–34

    Google Scholar 

  • Wiercigroch M, Budak E (2001) Sources of nonlinearities, chatter generation and suppression in metal cutting. Philos Trans R Soc Lond Math Phys Eng Sci 359:663–693

    MATH  Google Scholar 

  • Yang Y, Zhang W-H, Ma Y-C, Wan M (2016) Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. Int J Mach Tools Manuf 109:36–48

    Google Scholar 

  • Yoon M, Chin D (2005) Cutting force monitoring in the endmilling operation for chatter detection. Proc Inst Mech Eng Part b J Eng Manuf 219:455–465

    Google Scholar 

  • Yoon HU, Lee D-W (2018) Subplanner algorithm to escape from local minima for artificial potential function based robotic path planning. IJIFS INt J Fuzzy Logic Intell Syst 18:263–275

    Google Scholar 

  • Zhang Z, Li H, Meng G, Tu X, Cheng C (2016) Chatter detection in milling process based on the energy entropy of VMD and WPD. Int J Mach Tools Manuf 108:106–112

    Google Scholar 

  • Zhu W, Zhuang J, Guo B, Teng W, Wu F (2020) An optimized convolutional neural network for chatter detection in the milling of thin-walled parts. Int J Adv Manuf Technol 106:3881–3895

    Google Scholar 

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  1. Jaypee University of Engineering and Technology, Guna, M.P., India

    Pankaj Gupta & Bhagat Singh

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  1. Pankaj Gupta
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  2. Bhagat Singh
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Correspondence to Pankaj Gupta.

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Gupta, P., Singh, B. Investigation of tool chatter using local mean decomposition and artificial neural network during turning of Al 6061. Soft Comput 25, 11151–11174 (2021). https://doi.org/10.1007/s00500-021-05869-0

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