In-process detection and suppression of chatter in milling

doi:10.1016/0890-6955(92)90006-3

International Journal of Machine Tools and Manufacture

Volume 32, Issue 3, June 1992, Pages 329-347

https://doi.org/10.1016/0890-6955(92)90006-3 Get rights and content

Abstract

The use of a continuously variable spindle speed as a means of suppressing chatter in milling is investigated. A digital simulation model for dynamic milling is presented. The model includes runout, process damping and stiffness contributed by flank interference with and without tool wear, and spindle speed variation. Simulation and experimental results indicate that the stability of milling can be improved by early detection of chatter, and subsequent spindle speed oscillation to disturb the wave regeneration. Based on this investigation, an automatic chatter detection and suppression system is implemented on a milling machine.

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Cited by (194)

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Chatter suppression with productivity improvement by scheduling a C<sup>3</sup> continuous feedrate to match spindle speed variation
2023, Mechanical Systems and Signal Processing
Available spindle speed variation (SSV) methods, which are used to suppress regenerative chatter through disrupting the phase relationship between the inner and outer modulation on the machined surface, are usually performed in conjunction with the periodically varying feeds per tooth from the originally given constant feedrate. This article presents a new feedrate scheduling algorithm to match the SSV so that feed per tooth can be maximized during the whole machining. Concretely, a sinusoidal profile of feedrate is scheduled to match the designed SSV, meanwhile, the $C^{3}$ continuous condition is guaranteed within all kinematic constraints. The feedrate is scheduled as two parts, i.e. a jerk continuous acceleration profile with the constant feedrate and a continuous sinusoidal profile above the constant feedrate. The continuity of the junction between these two parts is achieved through scheduling a gradually varying sinusoidal profile. To guarantee the kinematic constraints, the derivatives of the sinusoidal amplitude variation are obtained by the properties of trigonometric functions. As a result, a constant feed per tooth, which actually corresponds to the maximum of the periodically varying feeds per tooth associated with all existing SSV methods, is obtained, while the speed still periodically varies. It proves that the proposed scheduling algorithm can improve the machining efficiency since the feed per tooth is kept at the maximum level in most machining periods, and this is a great advancement over all existing SSV methods. Experiments confirm that chatter can be well suppressed with the significantly reduced machining time by using the proposed method.
Automatic feature extraction for online chatter monitoring under variable milling conditions
2023, Measurement: Journal of the International Measurement Confederation
Chatter is an unpredictable self-excited vibration phenomenon in the milling process, which can seriously affect machining efficiency and quality. In the aerospace industry, the cutting process lasts for an extended period, and the cutting parameters continuously change. This paper presents an automated method for monitoring chatter in this field. Recurrence plot (RP) can accurately reflect dynamic changes in the cutting system, but its hyperparameters must be set in advance. This paper initially proposes a novel adaptive particle swarm algorithm (APSO) for calculating hyperparameters so that RP can be obtained automatically. Then, as the global and local features of RP show clear changes in different cutting states, a deep neural network architecture that can extract features from multiple scales is developed. Three categories of experiments are conducted to test the proposed method. Experimental results show that the proposed method can achieve accurate online chatter monitoring under different cutting conditions.
Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process
2023, Mechanical Systems and Signal Processing
Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data-driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as accelerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time–frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and automatically extracted features respectively. The first method uses two nonlinear dimensionless indicators, C₀ complexity and Power Spectral Entropy, of filtered signals. The second approach uses autoencoder for automatic feature extraction and Support Vector Machine as classifier for chatter identification. A series of milling experiments are conducted and chatters are intentionally created by changing the milling process parameters. Multiple internal signals are collected using software provided by the machine manufacturer. Results show that several internal CNC signals, such as the nominal current signal and the actual torque signal, can achieve comparable performance to external signals for chatter detection.
Investigation on an in-process chatter detection strategy for micro-milling titanium alloy thin-walled parts and its implementation perspectives
2023, Mechanical Systems and Signal Processing
Thin-walled micro parts are widely used in many fields, such as aviation, aerospace, precision engineering, and even micro-molds. Compared with other micro-fabrication technology, micro-milling can be considered as one of the most efficient 3D fabrication techniques. However, due to the vulnerable stiffness of thin-walled micro parts and micro tools, regenerative chatter is prone to occur during the micro-milling process. Severe chatter can lead to large fluctuations in cutting forces, resulting in deterioration of surface roughness/integrity and decreasing in tool life. Therefore, an in-process chatter detection strategy based on the feature extraction of micro-milling forces is proposed in this paper. Micro-milling force signals are measured and processed by advanced algorithms developed. After variational mode decomposition (VMD) for each group of cutting forces, the optimal intrinsic mode function (IMF) is adopted based on the Laplacian score (LS). The multi-scale permutation entropy (MSPE) values of the optimal IMF of each group are calculated, wherein the scale factor s, the embedding dimension m, and the delay time t are obtained by the genetic algorithm (GA). The obtained MSPE values are used as input vectors to train the support vector machine (SVM) classifier, which is used to monitor the stability states of the micro-milling processes. The effectiveness of the proposed strategy is compared with the other classic chatter detection methods and indicators in the micro-milling experiments of thin-walled parts. The results show that the method resulted from the strategy can extract cutting force features effectively and in-process detect chatters accurately in micro-milling thin-walled parts processes.
In-process impulse response of milling to identify stability properties by signal processing
2022, Journal of Sound and Vibration
In this work we present a quantitative measurement-based method to describe the stability behaviour of a periodic dynamical system. In-process response for impulse during milling is analysed to provide operational stability prediction. The proposed method combines chatter detection techniques with the theory of time-periodic delay differential equations applied for general milling operations. Signal processing based on dynamic modal decomposition approach not only presents qualitative properties (like stability/instability) but also quantifies a measure of stability through the determination of the Floquet multipliers. The corresponding monodromy operator of the milling process is approximated from the measured system’s response during machining operation. By changing the technological parameters, the variation of the modulus of the Floquet multipliers can be monitored. The stability limit can precisely be interpolated with the unitary multiplier; furthermore, the stability limit can be extrapolated while the manufacturing parameters remain in the chatter-free region. The presented approach is validated by numerical results and laboratory tests.

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View full text

In-process detection and suppression of chatter in milling

Abstract

Int. J. Mach. Tool Des. Res.

Machine-tool Vibrations

The effect of irregular tooth pitch on stability in milling

Increasing milling machine productivity by use of cutter with non-constant cutting-edge pitch

Ann. CIRP

Trans. ASME, J. Engng Ind.