The machining of thin-walled structures in aerospace manufacturing is frequently constrained by chatter, which significantly impairs dimensional accuracy, surface integrity, and tool life. This study provides a comprehensive review of the dynamic characteristics, prediction methodologies, and suppression strategies for milling chatter in thin-walled aerospace components. Techniques for characterizing the dynamic behavior of milling systems, including the tool, spindle, and workpiece, are examined, encompassing impact testing, frequency response function measurement, and finite element analysis. Chatter prediction approaches are reviewed, ranging from empirical and analytical models to finite element simulations and advanced intelligent prediction methods. Chatter suppression strategies are discussed across passive, semi-active, and active control technologies, highlighting their respective applicability and performance. Particular emphasis is placed on recent progress in digital twin technology, enabling real-time process monitoring and the integration of predictive control for chatter mitigation. A theoretical framework for chatter prediction and control in the context of digital twin assisted machining is presented. The review concludes with an outlook on future developments, emphasizing the need for high-fidelity multi-physics computational models, adaptive control algorithms, and full integration of digital twin technology to achieve more efficient, precise, and reliable machining of thin-walled structures.
IzamshahRMoJDingS.Hybrid deflection prediction on machining thin-wall monolithic aerospace components. Proc Inst Mech Eng Part B: J Eng Manuf2012; 226(4): 592–605.
2.
LiZSongQJinP, et al. Chatter suppression techniques in milling processes: a state of the art review. Chin J Aeronaut2024; 37(7): 1–23.
3.
GururajaSSinghK K.Bifurcation analysis for instability detection in high-speed micromilling of thin-walled Ti6Al4V structure. CIRP J Manuf Sci Technol2024; 49: 150–166.
4.
DengKYangLLuY, et al. Multitype chatter detection via multichannelinternal and external signals in robotic milling. Measurement2024; 229: 114417.
5.
MinisIYanushevskyRTemboA, et al. Analysis of linear and nonlinear chatter in milling. CIRP Ann-Manuf Technol1990; 39(1): 459–462.
6.
UçakNOuteiroJÇiçekA, et al. Comparative analysis of micro-milling performances of conventionally and additively manufactured ti6al4v alloys: experimental investigation and 3d modelling. CIRP J Manuf Sci Technol2024; 51: 213–235.
7.
RahimiMHHuynhHNAltintasY.On-line chatter detection in milling with hybrid machine learning and physics-based model. CIRP J Manuf Sci Technol2021; 35: 25–40.
8.
DongXTuGHuL, et al. Suppress chatter in milling of thin-walled parts via fixture with active support. J Vib Control2024; 30(5–6): 1286–1296.
9.
MöhringHCWiederkehrP.Intelligent fixtures for high performance machining. Procedia Cirp2016; 46: 383–390.
10.
ZhangXWangCLiuJ, et al. Robust active control based milling chatter suppression with perturbation model via piezoelectric stack actuators. Mech Syst Signal Process2019; 120: 808–835.
11.
BöttjerTTolaDKakavandiF, et al. A review of unit level digital twin applications in the manufacturing industry. CIRP J Manuf Sci Technol2023; 45: 162–189.
12.
QuintanaGCiuranaJ.Chatter in machining processes: A review. Int J Mach Tools Manuf2011; 51(5): 363–376.
13.
JiYBiQZhangS, et al. A new receptance coupling substructure analysis methodology to predict tool tip dynamics. Int J Mach Tools Manuf2018; 126: 18–26.
14.
DongHJiYWangX, et al. Stability analysis of thin-walled parts end milling considering cutting depth regeneration effect. Int J Adv Manuf Technol2021; 113: 3319–3328.
15.
FirroneCBerrutiT.An electromagnetic system for the non-contact excitation of bladed disks. Exp Mech2012; 52: 447–459.
16.
SatoRNoguchiSHokazonoT, et al. Time domain coupled simulation of machine tool dynamics and cutting forces considering the influences of nonlinear friction characteristics and process damping. Precis Eng2020; 61: 103–109.
17.
BerrutiTFirroneCMGolaMM.A test rig for noncontact traveling wave excitation of a bladed disk with underplatform dampers. J Eng Gas Turb and Power2011; 133(3): 103–109.
18.
TlaloliniDRitouMRabréauC, et al. Modeling and characterization of an electromagnetic system for the estimation of frequency response function of spindle. Mech Syst Signal Process2018; 104: 294–304.
19.
TlaloliniDRitouMRabréauC, et al. Effective characterization of an electromagnetic system for the non-contact spindle excitation and the estimation of frequency response functions. Mech Syst Signal Process2018; 104: 294–304.
SchmitzTLDuncanGS.Three-component receptance coupling substructure analysis for tool point dynamics prediction. J Manuf Sci Eng2005; 127(4): 781–790.
22.
ZhouGZhouKZhangJ, et al. Digital modeling-driven chatter suppression for thin-walled part manufacturing. J Intell Manuf2024; 35(1): 289–305.
23.
ErtürkAÖzgüvenHNBudakE.Analytical modeling of spindle-tool dynamics on machine tools using timoshenko beam model and receptance coupling for the prediction of tool point FRF. Int J Mach Tools Manuf2006; 46(15): 1901–1912.
24.
AlbertelliPGolettiMMonnoM.A new receptance coupling substructure analysis methodology to improve chatter free cutting conditions prediction. Int J Mach Tools Manuf2013; 72: 16–24.
25.
SchmitzTLDuncanGS.Receptance coupling for dynamics prediction of assemblies with coincident neutral axes. J Sound Vib2006; 289(4–5): 1045–1065.
26.
FilizSChengCHPowellK, et al. An improved tool-holder model for rcsa tool-point frequency response prediction. Precis Eng2009; 33(1): 26–36.
27.
SchmitzTHoneycuttAGomezM, et al. Multi-point coupling for tool point receptance prediction. J Manuf Process2019; 43: 2–11.
28.
ZhuLLiuC.Recent progress of chatter prediction, detection and suppression in milling. Mech Syst Signal Process2020; 143: 106840.
29.
FeiJXuFLinB, et al. State of the art in milling process of the flexible workpiece. Int J Adv Manuf Technol2020; 109: 1695–1725.
30.
CarneTStasiunasE.Lessons learned in modal testing-part 3: Transient excitation for modal testing, more than just hammer impacts. Exp Tech2006; 30(3): 69–79.
ThevenotVArnaudLDesseinG, et al. Integration of dynamic behaviour variations in the stability lobes method: 3d lobes construction and application to thin-walled structure milling. Int J Adv Manuf Technol2006; 27: 638–644.
33.
ThévenotVArnaudLDesseinG, et al. Influence of material removal on the dynamic behavior of thin-walled structures in peripheral milling. Mach Sci Technol2006; 10(3): 275–287.
34.
ArnaudLGonzaloOSeguyS, et al. Simulation of low rigidity part machining applied to thin-walled structures. Int J Adv Manuf Technol2011; 54: 479–488.
35.
TianYXiaoJLiuS, et al. Vibration and deformation suppression in mirror milling of thin-walled workpiece through a magnetic follow-up support fixture. J Manuf Process2023; 99: 168–183.
36.
CampaFDeLacalleLLCelayaA.Chatter avoidance in the milling of thin floors with bull-nose end mills: Model and stability diagrams. Int J Mach Tools Manuf2011; 51(1): 43–53.
37.
KiranKKayacanMC.Effect of material removal on workpiece dynamics in milling: Modeling and measurement. Precis Eng2019; 60: 506–519.
38.
MatthiasWÖzşahinOAltintasY, et al. Receptance coupling based algorithm for the identification of contact parameters at holder-tool interface. CIRP J Manuf Sci Technol2016; 13: 37–45.
39.
SchmitzTCorneliusAKarandikarJ, et al. Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability. CIRP Ann2022; 71(1): 321–324.
TrifunacMD.Comparisons between ambient and forced vibration experiments. Earthq Eng Struct D1972; 1(2): 133–150.
42.
PeetersBDe RoeckG.Reference-based stochastic subspace identification for output-only modal analysis. Mech Syst Signal Process1999; 13(6): 855–878.
43.
QuCXLiuYFYiTH, et al. Structural damping ratio identification through iterative frequency domain decomposition. J Struct Eng2023; 149(5): 04023042.
44.
ClarkMPScharfLL.Two-dimensional modal analysis based on maximumlikelihood. IEEE T Signal Proces1994; 42(6): 1443–1452.
45.
SteffensenMTDöhlerMTcherniakD, et al. Variance estimation of modal parameters from the poly-reference least-squares complex frequency-domain algorithm. Mech Syst Signal Process2025; 223: 111905.
46.
LiBCaiHMaoX, et al. Estimation of cnc machine-tool dynamic parameters based on random cutting excitation through operational modal analysis. Int J Mach Tools Manuf2013; 71: 26–40.
47.
ZaghbaniISongmeneV.Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis. Int J Mach Tools Manuf2009; 49(12–13): 947–957.
48.
PowałkaBJemielniakK.Stability analysis in milling of flexible parts based on operational modal analysis. CIRP J Manuf Sci Technol2015; 9: 125–135.
49.
AltintasYBudakE.Analytical prediction of stability lobes in milling. CIRP Ann1995; 44(1): 357–362.
50.
TobiasSFishwickW.Theory of regenerative machine tool chatter. Engineer1958; 205(7): 199–203.
51.
SmithSTlustyJ.Update on high-speed milling dynamics. J Eng Ind1990; 112: 142–149.
52.
InspergerTStépánG.Semi-discretization method for delayed systems. Int J Numer Meth Eng2002; 55(5): 503–518.
53.
DingYZhuLZhangX, et al. A full-discretization method for prediction of milling stability. Int J Mach Tools Manuf2010; 50(5): 502–509.
54.
DingYZhuLZhangX, et al. Numerical integration method for prediction of milling stability. J Manuf Sci Eng2011; 133(3): 031005.
55.
BaylyPHalleyJMannBP, et al. Stability of interrupted cutting by temporal finite element analysis. J Manuf Sci Eng2003; 125(2): 220–225.
56.
ButcherEAMaHBuelerE, et al. Stability of linear time-periodic delay-differential equations via chebyshev polynomials. Int J Numer Meth Eng2004; 59(7): 895–922.
57.
MunoaJBeudaertXDombovariZ, et al. Chatter suppression techniques in metal cutting. CIRP Ann2016; 65(2): 785–808.
58.
AfazovSScrimieriD.Chatter model for enabling a digital twin in machining. Int J Adv Manuf Technol2020; 110: 2439–2444.
59.
SunLZhengKLiaoW. Chatter suppression and stability analysis of rotary ultrasonic milling titanium alloy thin-walled workpiece. Int J Adv Manuf Technol2022 : 1–12.
60.
ZhangZLiHLiuX, et al. Chatter mitigation for the milling of thin-walled workpiece. Int J Mech Sci2018; 138: 262–271.
61.
SongQLiuZWanY, et al. Application of sherman-morrison-woodbury formulas in instantaneous dynamic of peripheral milling for thin-walled component. Int J Mech Sci2015; 96: 79–90.
62.
LiZWangZShiX.Fast prediction of chatter stability lobe diagram for milling process using frequency response function or modal parameters. Int J Adv Manuf Technol2017; 89: 2603–2612.
63.
LöserMOttoAIhlenfeldtS, et al. Chatter prediction for uncertain parameters. Adv Manuf2018; 6(3): 319–333.
64.
ZhengYZhaoZXuB, et al. A method to predict chatter stability accurately in milling thin-walled parts by considering force-induced deformation. J Manuf Process2023; 106: 552–563.
65.
WangLLiWYuG.Time domain study on the construction mechanism of milling stability lobe diagrams with multiple modes. J Manuf Sci Eng2022; 144(2): 021007.
66.
DingYZhuL.Investigation on chatter stability of thin-walled parts considering its flexibility based on finite element analysis. Int J Adv Manuf Technol2018; 94: 3173–3187.
67.
LiuYGlassG.Effects of wall thickness and geometric shape on thin-walled parts structural performance. Thin Walled Struct2011; 49(1): 223–231.
68.
ÁdányS.Modal identification of thin-walled members by using the constrained finite element method. ThinWalled Struct2019, 140: 31–42.
69.
RustWSchweizerhofK.Finite element limit load analysis of thin-walled structures by ANSYS (implicit), LS-DYNA (explicit) and in combination. Thin Walled Struct2003; 41(2–3): 227–244.
70.
Pacheco-ChérrezJCárdenasDProbstO.Experimental detection and measurement of crack-type damage features in composite thin-wall beams using modal analysis. Sensors2021; 21(23): 8102.
71.
DoomsDDegrandeGDe RoeckG, et al. Finite element modelling of a silo based on experimental modal analysis. Eng Struct2006; 28(4): 532–542.
72.
TianWRenJZhouJ, et al. Dynamic modal prediction and experimental study of thin-walled workpiece removal based on perturbation method. Intl J Adv Manuf Technol2018; 94(5): 2099–2113.
73.
BakerJRouchK.Use of finite element structural models in analyzing machine tool chatter. Finite Elem Anal Des2002; 38(11): 1029–1046.
74.
MahnamaMMovahhedyM.Prediction of machining chatter based on fem simulation of chip formation under dynamic conditions. Int J Mach Tools Manuf2010; 50(7): 611–620.
75.
PrasadCSKolmanRPešekL.Meshfree reduced order model for turbomachinery blade flutter analysis. Int J Mech Sci2022; 222: 107222.
76.
SinghKKSinghR.Chatter stability prediction in high-speed micromilling of ti6al4v via finite element based microend mill dynamics. Adv Manuf2018; 6: 95–106.
77.
YangYZhangWHMaYC, et al. Chatter prediction for the peripheral milling of thin-walled workpieces with curved surfaces. Int J Mach Tools Manuf2016; 109: 36–48.
78.
JoshiSNBolarG.Three-dimensional finite element based numerical simulation of machining of thin-wall components with varying wall constraints. J Inst Eng India Ser C2017; 98: 343–352.
79.
LiangCYuSMaY, et al. Theoretical and experimental studies of chatter in turning and machining stainless steel workpiece. Int J Adv Manuf Technol2021; 117(11): 3755–3776.
80.
WangCZhangXChenX, et al. Time-varying chatter frequency characteristics in thin-walled workpiece milling with b-spline wavelet on interval finite element method. J Manuf Sci Eng2019; 141(5): 051008.
81.
LiWWangLYuG, et al. Time-varying dynamics updating method for chatter prediction in thin-walled part milling process. Mech Syst Signal Process2021; 159: 107840.
82.
TuysuzOAltintasY.Frequency domain updating of thin-walled workpiece dynamics using reduced order substructuring method in machining. J Manuf Sci Eng2017; 139(7): 071013.
83.
LamraouiMBarakatMThomasM, et al. Chatter detection in milling machines by neural network classification and feature selection. J Vib Control2015; 21(7): 1251–1266.
84.
YueCXXieZLLiuXL, et al. Chatter prediction of milling process for titanium alloy thin-walled workpiece based on emd-svm. J Adv Manuf Sci Technol2022; 2(2): 2022010.
85.
WangPBaiQChengK, et al. Investigation on an in-process chatter detection strategy for micro-milling titanium alloy thin-walled parts and its implementation perspectives. Mech Syst Signal Process2023; 183: 109617.
86.
TranMQLiuMKTranQV.Milling chatter detection using scalogram and deep convolutional neural network. Int J Adv Manuf Technol2020; 107(3): 1505–1516.
87.
ZhuWZhuangJGuoB, et al. An optimized convolutional neural network for chatter detection in the milling of thin-walled parts. Int J Adv Manuf Technol2020; 106(9): 3881–3895.
88.
JauhariKRahmanAZAl HudaM, et al. Building digital-twin virtual machining for milling chatter detection based on vmd,synchro-squeeze wavelet, and pre-trained network cnns with vibration signals. J Intell Manuf2024; 35(7): 3083–3114.
89.
ChenGZhengQ.Online chatter detection of the end milling based on wavelet packet transform and support vector machine recursive feature elimination. Int J Adv Manuf Technol2018; 95: 775–784.
90.
ZhaoMYueCLiuX.Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion. Int J Adv Manuf Technol2023; 125(9): 3925–3941.
91.
TranMQLiuMKElsisiM.Effective multi-sensor data fusion for chatter detection in milling process. ISA T2022; 125: 514–527.
92.
LiuHMiaoHWangC, et al. Online chatter identification for thin-walled parts machining based on improved multisensor signal fusion and multiscale entropy. IEEE T Instrum Meas2023; 72: 1–13.
93.
LiuGWangYHuangB, et al. The intelligent monitoring technology for machining thin-walled components: a review. Machines2024; 12(12): 876.
94.
LiDDZhangWMLiYS, et al. Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing. Adv Manuf2021; 9: 22–33.
95.
WangXSongQLiuZ.Precise chatter monitoring of thin-walled component milling process based on parametric time-frequency transform method. J Mater Process Technol2020; 283: 116712.
96.
LuYMaHSunY, et al. An interpretable anti-noise convolutional neural network for online chatter detection in thin-walled parts milling. Mech Syst Signal Process2024, 206: 110885.
97.
LyuZJLuQSongYM, et al. Data-driven decision-making in the design optimization of thin-walled steel perforated sections: a case study. Adv Civ Eng2018; 2018(1): 6326049.
98.
HanZZhuoYYanY, et al. Chatter detection in milling of thin-walled parts using multi-channel feature fusion and temporal attention-based network. Mech Syst Signal Process2022; 179: 109367.
99.
WangRSongQLiuZ, et al. A novel unsupervised machine learning-based method for chatter detection in the milling of thin-walled parts. Sensors2021; 21(17): 5779.
100.
YanSSunY.Early chatter detection in thin-walled workpiece milling process based on multi-synchrosqueezing transform and feature selection. Mech Syst Signal Process2022; 169: 108622.
101.
MouWZhuSJiangZ, et al. Vibration signal-based chatter identification for milling of thin-walled structure. Chin J Aeronaut2022; 35(1): 204–214.
102.
HaoYZhuLYanB, et al. Milling chatter detection with WPD and power entropy for Ti-6Al-4V thin-walled parts based on multi-source signals fusion. Mech Syst Signal Process2022; 177: 109225.
103.
InspergerTStépánG.Updated semi-discretization method for periodic delay-differential equations with discrete delay. Int J Numer Meth Eng2004; 61(1): 117–141.
104.
JinGQiHCaiY, et al. Stability prediction for milling process with multiple delays using an improved semi-discretization method. Math Methods Appl Sci2016; 39(4): 949–958.
105.
UnverHOSenerB.A novel transfer learning framework for chatter detection using convolutional neural networks. J Intell Manuf2023; 34(3): 1105-1124.
106.
SunYHeJMaH, et al. Online chatter detection considering beat effect based on Inception and LSTM neural networks. Mech Syst Signal Process2023; 184: 109723.
107.
QuSZhaoJWangT.Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. Int J Adv Manuf Technol2017; 89(5): 2399–2409.
108.
AhmadiK.Bayesian updating of modal parameters for modeling chatter in turning. CIRP J Manuf Sci Technol2022; 38: 724–736.
109.
AoyamaTKakinumaY.Development of fixture devices for thin and compliant workpieces. CIRP Ann2005; 54(1): 325–328.
110.
KolluruKAxinteD.Novel ancillary device for minimising machining vibrations in thin wall assemblies. Int J Mach Tools Manuf2014; 85: 79-86.
111.
ZengSWanXLiW, et al. A novel approach to f ixture design on suppressing machining vibration of flexible workpiece. Int J Mach Tools Manuf2012; 58: 29–43.
112.
WanXJZhangYHuangXD.Investigation of influence of fixture layout on dynamic response of thin-wall multi-framed work-piece in machining. Int J Mach Tools Manuf2013; 75: 87–99.
113.
XiangJYiJ.Deformation mechanism in wax supported milling of thin-walled structures based on milling forces stability. CIRP J Manuf Sci Technol2021; 32: 356–369.
114.
WangTZhaJJiaQ, et al. Application of lowmelting alloy in the fixture for machining aeronautical thin-walled component. Int J Adv Manuf Technol2016; 87: 2797–2807.
115.
LiuHWangCHanL, et al. The influence of ice-based fixture on suppressing machining-induced deformation of cantilever thin-walled parts: a novel and green fixture. Int J Adv Manuf Technol2021; 117: 329–341.
116.
MaJZhangDWuB, et al. Vibration suppression of thin-walled workpiece machining considering external damping properties based on magnetorheological fluids flexible fixture. Chin J Aeronaut2016; 29(4): 1074–1083.
117.
LiuHWangJLuoQ, et al. Effect of controllable magnetic field-induced mrf solidification on chatter suppression of thin-walled parts. Int J Adv Manuf Technol2020; 109: 2881–2890.
118.
WangXMaPPengX, et al. Study on vibration suppression performance of a flexible fixture for a thin-walled casing. Int J Adv Manuf Technol2020; 106: 4281–4291.
119.
YuanHWanMYangY, et al. Mitigation of chatter in thin-wall milling by using double-side support device. Int J Adv Manuf Technol2021; 115(1): 213–232.
120.
FeiJLinBXiaoJ, et al. Investigation of moving fixture on deformation suppression during milling process of thin-walled structures. J Manuf Process2018; 32: 403–411.
121.
LiuHBoQZhangH, et al. Analysis of Q-factor’s identification ability for thin-walled part flank and mirror milling chatter. Int J Adv Manuf Technol2018; 99: 1673–1686.
122.
WangYBoQLiuH, et al. Mirror milling chatter identification using Q-factor and SVM. Int J Adv Manuf Technol, 2018, 98: 1163–1177.
123.
ChengKNiuZCWangRC, et al. Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives. Chin J Mech Eng2017; 30: 1162-1176.
124.
KhaghaniAChengK.Investigation on an innovative approach for claming contact lens mould inserts in ultraprecision machining using an adaptive precision chuck and its application perspectives. Int J Adv Manuf Technol2020; 111: 839–850.
125.
WangZZouLSuX, et al. Hybrid force/position control in workspace of robotic manipulator in uncertain environments based on adaptive fuzzy control. Robot Auton Syst2021; 145: 103870.
126.
XieFChongZLiuXJ, et al. Precise and smooth contact force control for a hybrid mobile robot used in polishing. Robot Comput Integr Manuf2023; 83: 102573.
127.
BoQWangPHouB, et al. Mirror supporting device based on magnetorheological fluid and control strategy based on force signal feedback for mirror milling. Mech Syst Signal Process2024; 212: 111309.
128.
XiaoJLZhaoSLGuoH, et al. Research on the collaborative machining method for dual-robot mirror milling. Int J Adv Manuf Technol2019; 105(10): 4071–4084.
129.
ChenTLiDMaW, et al. Design of a composite viscous damper and application on cylindrical thin-walled part milling. Proc Inst Mech Eng Part B: J Eng Manuf2023; 237(1–2): 134–143.
130.
MaruiEEmaSHashimotoM, et al. Plate insertion as a means to improve the damping capacity of a cutting tool system. Int J Mach Tools Manuf1998; 38(10–11): 1209–1220.
131.
ZiegertJCStanislausCSchmitzTL, et al. Enhanced dampinginlongslender end mills. J Manuf Process2006; 8(1): 39–46.
132.
GalarzaFAMde AlbuquerqueMVAntonialliAÍS, et al. Design and experimental evaluation of an impact damper to be used in a slender end mill tool in the machining of hardened steel. Int J Adv Manuf Technol2020; 106: 2553-2567.
133.
SaciottoVRDinizAE.An experimental evaluation of particle impact dampers applied on the tool for milling of hardened steel complex surface. Int J Adv Manuf Technol2022; 119(11): 7579–7597.
134.
YangYWangYLiuQ.Design of a milling cutter with large length-diameter ratio based on embedded passive damper. J Vib Control2019; 25(3): 506–516.
135.
MaWYuJYangY, et al. Optimization and tuning of passive tuned mass damper embedded in milling tool for chatter mitigation. J Manuf Mater Process2020; 5(1): 2.
136.
DrazUHussainS.Machining-induced geometric errors in thin-walled parts-a review of mitigation strategies and development of application guidelines. Int J Adv Manuf Technol2025; : 1–40.
137.
BeudaertXErkorkmazKMunoaJ.Portable damping system for chatter suppression on flexible workpieces. CIRP Ann2019; 68(1): 423–426.
138.
SimELeeSW.Active vibration control of flexible structures with acceleration feedback. J Guid Control1993; 16(2): 413–415.
139.
SongGSchmidtSAgrawalB.Experimental robustness study of positive position feedback control for active vibration suppression. J Guid Control2002; 25(1): 179a–182.
140.
JnifeneA.Active vibration control of flexible structures using delayed position feedback. Syst Control Lett2007; 56(3): 215–222.
141.
HadiMSDarusIZAb TalibMH, et al. Vibration suppression of the horizontal flexible plate using proportional-integral-derivative controller tuned by particle swarm optimization. J Low Freq Noise, Vib and Act Control2021; 40(3): 1540–1557.
142.
LiXLiuSWanS, et al. Active suppression of milling chatter based on lqr-anfis. Int J Adv Manuf Technol2020; 111: 2337–2347.
143.
RuttanatriPColeMOPongvuthithumR.Structural vibration control using delayed state feedback via lmi approach: with application to chatter stability problems. Int J Dyn Control2021; 9(1): 85–96.
144.
MunoaJMancisidorILoixN, et al. Chatter suppression in ram type travelling column milling machines using a biaxial inertial actuator. CIRP Ann2013; 62(1): 407–410.
145.
WanSLiXSuW, et al. Active chatter suppression for milling process with sliding mode control and electromagnetic actuator. Mech Syst Signal Process2020; 136: 106528.
146.
WangCZhangXLiuY, et al. Stiffness variation method for milling chatter suppression via piezoelectric stack actuators. Int J Mach Tools Manuf2018; 124: 53–66.
147.
DenkenaBBickelWPonickB, et al. Dynamic analysis of a motor-integrated method for a higher milling stability. Prod Eng2011; 5: 691–699.
KongJDingBWangW, et al. An electromagnetic semi-active dynamic vibration absorber for thin-walled workpiece vibration suppression in mirror milling. Appl Math Mech2024, 45(8): 1315–1334.
150.
MuhammadBBWanMLiuY, et al. Active damping of milling vibration using operational amplifier circuit. Chin J Mech Eng2018; 31: 1–8.
151.
WangSSongQLiuZ.Vibration suppression of thin-walled workpiece milling using a time-space varying pd control method via piezoelectric actuator. Int J Adv Manuf Technol2019; 105: 2843–2856.
152.
DuJLongX.Chatter suppression for milling of thin-walled workpieces based on active modal control. J Manuf Process2022; 84: 1042–1053.
153.
NamSHayasakaTJungH, et al. Proposal of novel chatter stability indices of spindle speed variation based on its chatter growth characteristics. Precis Eng2020; 62: 121–133.
154.
SeguySInspergerTArnaudL, et al. On the stability of high-speed milling with spindle speed variation. Int J Adv Manuf Technol2010; 48: 883–895.
155.
ZatarainMBediagaIMunoaJ, et al. Stability of milling processes with continuous spindle speed variation: analysis in the frequency and time domains, and experimental correlation. CIRP Ann2008; 57(1): 379–384.
156.
KalinskiKJGalewskiMA.Optimal spindle speed determination for vibration reduction during ball-end milling of flexible details. Int J Mach Tools Manuf2015; 92: 19–30.
157.
SastrySKapoorSGDeVorRE.Floquet theory based approach for stability analysis of the variable speed face-milling process. J Manuf Sci Eng2002; 124(1): 10–17.
158.
NamachchivayaSBeddini. Spindle speed variation for the suppression of regenerative chatter. J Nonlin Sci2003; 13: 265–288.
159.
WangCZhangXYanR, et al. Multiharmonicspindlespeed variation for milling chatter suppression and parameters optimization. Precis Eng2019; 55: 268–274.
160.
WangCZhangXLiuJ, et al. Multi harmonic and random stiffness excitation for milling chatter suppression. Mech Syst Signal Process2019; 120: 777–792.
161.
ZhangHTWuYHeD, et al. Model predictive control to mitigate chatters in milling processes with input constraints. Int J Mach Tools Manuf2015; 91: 54–61.
162.
FallahMMoetakef-ImaniB.Adaptive inverse control of chatter vibrations in internal turning operations. Mechl Syst Signal Process2019; 129: 91–111.
163.
KalińskiKJGalewskiMA.Vibration surveillance supported by hardware-in-the-loop simulation in milling flexible workpieces. Mechatronics2014; 24(8): 1071–1082.
164.
SimsNMannBHuyananS.Analytical prediction of chatter stability for variable pitch and variable helix milling tools. J Sound Vib2008; 317(3–5): 664–686.
165.
YusoffARSimsND.Optimisation of variable helix tool geometry for regenerative chatter mitigation. Int J Mach Tools Manuf2011; 51(2): 133–141.
166.
AltintasYStepanGBudakE, et al. Chatter stability of machining operations. J Manuf Sci Eng2020; 142(11): 110801.
167.
NieWZhengMZhangW, et al. Analytical prediction of chatter stability with the effect of multiple delays for variable pitch end mills and optimization of pitch parameters. Int J Adv Manuf Technol2023; 124(7): 2645–2658.
168.
JinGQiHJLouLY.Stability prediction and dynamics analysis of milling with variable pitch cutter in low radial immersion. Adv Mater Research2014; 952: 176–180.
169.
WangYWangTYuZ, et al. Chatter prediction for variable pitch and variable helix milling. Shock Vib2015; 2015(1): 419172.
170.
OttoARauhSIhlenfeldtS, et al. Stability of milling with non-uniform pitch and variable helix tools. Int J Adv Manuf Technol2017; 89: 2613–2625.
171.
KocaRBudakE.Optimization of serrated end mills for reduced cutting energy and higher stability. Procedia CIRP2013; 8: 570–575.
172.
FarahaniNDAltintasY.Chatter stability of serrated milling tools in frequency domain. J Manuf Sci Eng2022; 144(3): 031013.
173.
YangYSongMGaoS, et al. Analysis of milling chatter stability and experimental study on cutting performance with discrete-edge end milling cutters. Int J Adv Manuf Technol2024; 140: 2797–2812.
174.
DengJWangFLinY, et al. An unequal pitch ball-end milling cutter with micro-groove structure for suppressing cfrp milling chatter based on coordinated regulation of time delay effect and milling force. Int J Adv Manuf Technol2025; 137(3): 1437–1456.
175.
TehranizadehFBerenjiKRBudakE.Dynamics and chatter stability of crest-cut end mills. Int J Mach Tools Manuf2021; 171: 103813.
176.
TehranizadehFBerenjiKRYıldızS, et al. Chatter stability of thin-walled part machining using special end mills. CIRP Ann2022; 71(1): 365–368.
177.
LiuYLiuZSongQ, et al. Development of constrained layer damping toolholder to improve chatter stability in end milling. Int J Mech Sci2016; 117: 299–308.
178.
ShaftoMConroyMDoyleR, et al. Draft modeling, simulation, information technology & processing roadmap. Technol Area2010; 11: 1–32.
TaoFZhangMChengJ, et al. Digital twin workshop: a new paradigm for future workshop. Comput Integr Manuf Syst2017; 23(1): 1–9.
181.
ZhuZXiXXuX, et al. Digital twin-driven machining process for thin-walled part manufacturing. J Manuf Syst2021; 59: 453–466.
182.
LiuMFangSDongH, et al. Review of digital twin about concepts, technologies, and industrial applications. J Manuf Syst2021; 58: 346–361.
183.
ChoiSHParkKBRohDH, et al. An integrated mixed reality system for safety-aware human-robot collaboration using deep learning and digital twin generation. Robot Comput-Integr Manuf2022; 73: 102258.
184.
FanWGuoXFengE, et al. Digital twin-driven mixed reality framework for immersive teleoperation with haptic rendering. IEEE Robot Autom Lett2023; 8(12): 8494–8501.
185.
LiuSLuSLiJ, et al. Machining process-oriented monitoring method based on digital twin via augmented reality. Int J Adv Manuf Technol2021; 113: 3491–3508.
186.
XuJShiMMaH, et al. Agent-based modeling system for real-time characterization of film thickness in digital twin coating. Proc Inst Mech Eng Part B: J Eng Manuf2025; 239(11): 1635-1645.
187.
González-HerbónRGonzález-MateosGRodríguez-OssorioJR, et al. An approach to develop digital twins in industry. Sensors2024; 24(3): 998.
188.
BakhshandehPMohammadiYAltintasY, et al. Digital twin assisted intelligent machining process monitoring and control. CIRP J Manuf Sci Technol2024; 49: 180–190.
189.
WangZLiuSLiH, et al. A novel method for updating time-varying information of milling thin-walled components based on digital twin model. IEEE Sens J2023; 24(3): 2531–2546.
190.
DongLHuTYueP, et al. A product performance rapid simulation approach driven by digital twin data: Part 1. for variable product structures. Adv Eng Inf2024; 59: 102337.
191.
DongLHuTLiJ, et al. A product performance rapid simulation approach driven by digital twin data: Part 2. for variable operating conditions. Adv Eng Inf2024; 59: 102336.
192.
QiTFFangHRChenYF, et al. Research on digital twin monitoring system for large complex surface machining. J Intell Manuf2024; 35(3): 977–990.
193.
LiuSLuYLiJ, et al. Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model. Robot Comput-Integr Manuf2021; 71: 102123.
194.
AbedMGamerosAMohammadA, et al. Swift feedback and immediate error control using a lightweight simulation approach-a case study of the digital-twin-in-the-loop for machining thin-wall structures. J Manuf Syst2023; 71: 309–322.
195.
ZhouYXingTSongY, et al. Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling. J Manuf Syst2021; 58: 22–35.
196.
WardRSunCDominguez-CaballeroJ, et al. Machining Digital Twin using real-time model-based simulations and lookahead function for closed loop machining control. Int J Adv Manuf Technol, 2021; 117(11): 3615–3629.
197.
ZhaoWLiRLiuX, et al. Construction method of digital twin system for thin-walled workpiece machining error control based on analysis of machine tool dynamic characteristics. Machines2023; 11(6): 600.
198.
SongJJinHWangX, et al. Research on digital twin model for milling parameter optimization of thin-walled parts. Int J Adv Manuf Technol, 2025, 136(7): 3803–3819.
199.
LuYYueCLiuX, et al. Research on digital twin monitoring system during milling of large parts. J Manuf Syst, 2024, 77: 834–847.
200.
ZhouZWaltherMVerlA.Learning digital twin: a case study on chatter suppression based on a time-varying stability lobe diagram. Int J Adv Manuf Technol2025; 137(5): 2563–2577.
