In machining systems, chatter is a harmful dynamic instability phenomenon that can cause high-frequency oscillations between the tool and the workpiece, exacerbate tool wear, and affect machining quality. To achieve efficient and accurate vibration monitoring, this study proposes an integrated optimized variational mode decomposition (VMD) and multi-scale permutation entropy (MPE) milling vibration signal analysis method—ITSLWOA-VMD-MPE. This method combines the Improved Whale Optimization Algorithm (ITSLWOA) to achieve adaptive signal decomposition and utilizes MPE for chatter state recognition. Firstly, Tent chaotic mapping, sine adaptive weighting, and Levy flight optimization WOA are introduced to enhance its search efficiency and global exploration capability. Secondly, ITSLWOA is used to adaptively optimize VMD parameters and accurately extract vibration signals during the milling process. Finally, combining MPE to quantify the evolution of chatter states can improve the accuracy of chatter identification. Simulation and experimental results show that this method can efficiently and accurately decompose milling signals, enhance the ability to extract chatter features, and effectively improve the robustness of chatter recognition.
CaoHLeiYHeZ.Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform. Int J Mach Tools Manuf2013; 69: 11–19.
2.
CaoHZhouKChenX.Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators. Int J Mach Tools Manuf2015; 92: 52–59.
3.
CaoHYueYChenX, et al. Chatter detection based on synchrosqueezing transform and statistical indicators in milling process. Int J Adv Manuf Technol2018; 95: 961–972.
4.
SunYXiongZ.An optimal weighted wavelet packet entropy method with application to real-time chatter detection. Trans Mechatron2016; 21(4): 2004–2014.
5.
MatthewDECaoHShiJ. Advancing chatter detection: Harnessing the strength of wavelet synchrosqueezing transform and Hilbert-Huang transform techniques. J Manuf Process2024; 127: 5–7.
6.
JiYWangXLiuZ, et al. Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation. J Sound Vib2018; 433: 138–159.
7.
ShrivastavaYSinghB.Tool chatter prediction based on empirical mode decomposition and response surface methodology. Measurement2021; 173: 108585.
8.
FengJSunZJiangZ, et al. Identification of chatter in milling of Ti-6Al-4V titanium alloy thin-walled workpieces based on cutting force signals and surface topography. Int J Adv Manuf Technol2016; 82: 1909–1920.
9.
YangKWangGDongY, et al. Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Process2019; 115: 238–254.
10.
MirjaliliSLewisA.The whale optimization algorithm. Adv Eng Softw2016; 95: 51–67.
11.
MouWZhuSJiangZ, et al. Vibration signal-based chatter identification for milling of thin-walled structure. Chin J Aeronaut2022; 35(1): 204–214.
12.
WangYMarkertRXiangJ, et al. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system. Mech Syst Signal Process2015; 60-61: 243–251.
13.
WangYZhangMTangX, et al. A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot. J Intell Manuf2022; 33: 1483–1502.
14.
LiuBLiuCZhouY, et al. A chatter detection method in milling based on gray wolf optimization VMD and multi-entropy features. Int J Adv Manuf Technol2023; 125(1): 831–854.
15.
Arrieta PaterninaMRTripathyRKZamora-MendezA, et al. Identification of electromechanical oscillatory modes based on variational mode decomposition. Elect Power Syst Res2019; 167: 71–85.
16.
ShuiXRongZDanB, et al. Tool wear state identification based on the IWOA-VMD feature selection method. Machines2024; 12(3): 184.
17.
PengDLiHOuJ, et al. Milling chatter identification by optimized variational mode decomposition and fuzzy entropy. Int J Adv Manuf Technol2022; 121(9): 6111–6124.
18.
TuJChenHLiuJ, et al. Evolutionary biogeography-based whale optimization methods with communication structure: towards measuring the balance. Knowl Syst2021; 212: 106642.
19.
LingYZhouYLuoQ.Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access2017; 5: 6168–6186.
20.
WuLLiZHanY, et al. Signal processing using wavelet transform and short-time Fourier transform based on spectral-scanning FMCW LiDAR. In: 2022 Asia communications and photonics conference (ACP), Shenzhen, China, 2022: 789-791. IEEE.
21.
ZhuLLiuCJuC, et al. Vibration recognition for peripheral milling thin-walled workpieces using sample entropy and energy entropy. Int J Adv Manuf Technol2020; 108: 3251–3266.
22.
LiKHeSLiB, et al. A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting. Mech Syst Signal Process2020; 135: 106385.
23.
ChenYChenYDaiQ.Gear compound fault detection method based on improved multiscale permutation entropy and local mean decomposition. J Vibroengineering2021; 23(5): 1171–1183.
24.
DragomiretskiyKZossoD.Variational mode decomposition. IEEE Trans Signal Process2014; 62(3): 531–544.
25.
ChakrabortySSharmaSSahaAK, et al. SHADE–WOA: a metaheuristic algorithm for global optimization. Appl Soft Comput2021; 113: 107866.
ViswanathanGM.Ecology: fish in Lévy-flight foraging. Nature2010; 465(7301): 1018–1019.
28.
LeccardiM. Comparison of three algorithms for Levy noise generation[C]. In: Proceedings of 5th EUROMECH nonlinear dynamics conference, Eindhoven, The Netherlands, 2005: 1–14.
29.
LiuCZhuLNiC.Chatter detection in milling process based on VMD and energy entropy. Mech Syst Signal Process2018; 105: 169–182.
30.
WangWLiHWangC, et al. An improved ultrasonic computerized tomography (UCT) technique for damage localization based on compressive sampling (CS) theory. Struct Control Health Monit2022; 29(6): e2938.
31.
WangXYaoYGaoC.Wasserstein distance-EEMD enhanced multi-head graph attention network for rolling bearing fault diagnosis under different working conditions. Maint Reliab Eksploat Niezawodn2024; 26(2): 184037.
32.
CabreraCGAraujoACCastelloDA.On the wavelet analysis of cutting forces for chatter identification in milling. Adv Manuf2017; 5: 130–142.
33.
LiuXWangZLiM, et al. Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy. Int J Adv Manuf Technol2021; 114: 2849–2862.
