Chatter identification in thin-wall milling using an adaptive variational mode decomposition method combined with the decision tree model

Wang, Ruoqi; Niu, Jinbo; Sun, Yuwen

doi:10.1177/0954405420933705

Cited by 9 publications

(5 citation statements)

References 31 publications

(39 reference statements)

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“…The process uses a decision tree to calculate the chattering threshold after adaptive variational mode decomposition divides the original signal into many sub-signals. The results of the experiments demonstrated that the approach can accurately and successfully identify chatter [5]. Jin et al proposed a stability prediction method based on a dynamics model.…”

Section: Related Workmentioning

confidence: 98%

Frequency monitoring analysis of milling edge chatter in composites using WPD for frequency band selection

Liu

2024

J. vibroeng.

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In the milling process of composite materials, the initial chatter frequency is not obvious and is easily swamped by the rest of the signals, making frequency monitoring difficult, so the study proposes a chatter frequency monitoring method based on frequency cancellation algorithms and wavelet packet decomposition. The results of the experiments shown that the frequency cancellation algorithm can successfully remove invalid signals, such as spindle rotation frequency and cutter tooth frequency, and only keep the necessary signals, at which point the chattering frequency may be observed at a frequency of roughly 1333 Hz. The influence of the frequency bands s5, s9, s10, s12, and s13 after de-frequency removal should be avoided because they all have a low energy share of roughly 23 %, 0.9 %, 5 %, 10 %, and 16 %, respectively, and are less sensitive to chatter. For milling edge depths of 0.5 mm, 2 mm, and 4 mm, the average chatter thresholds were around 3.27, 2.9, and 2.89, respectively. It was challenging to pinpoint the chatter of the system because the empirical modal decomposition observed an average chatter energy entropy of just 1.55 and found that its fluctuations at the milling edge depth junction were insignificant. On the other hand, the chattering could be plainly seen since the energy entropy experienced a substantial and dramatic fluctuation at the intersection of the milling edge depth when it was about 2.9, 2.6, and 2.5, respectively. The experimental findings demonstrated that the frequency cancellation technique and wavelet packet decomposition-based chattering frequency monitoring approach can precisely track the chattering state of the system.

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Section: Related Workmentioning

confidence: 98%

Frequency monitoring analysis of milling edge chatter in composites using WPD for frequency band selection

Liu

2024

J. vibroeng.

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“…Cyclostationary-based indicators were proposed in the angular domain from the periodic and residual parts of angular speed and cutting force signals for chatter detection, and the indicator based on IAS is recommended as it does not require additional sensors [84]. The application of adaptive variational mode decomposition for chatter detection has been lately reported in [142] and [198]. Mishra and Singh [87,[340][341][342][343]] investigated a spline-based local mean decomposition technique, while Zhang et al [137] used a morphological empirical wavelet transform (EWT).…”

Section: Time-frequency Domain Analysismentioning

confidence: 99%

“…Entropy features represent the signal randomness and complexity. Some of them have been reported for chatter detection, including permutation entropy (PE) [134,157], Rényi entropy (RE) [162], Sample entropy (SampEn) [133,142,186], approximate entropy (ApEn) [88,133,169,170,183] and dispersion entropy [151]. Tran et al [154] utilized fuzzy entropy for feature selection.…”

Section: Feature Generationmentioning

confidence: 99%

“…Approximate entropy [88,133,169,170,183] Sample entropy and others [133,141,142,162,171,186,198,223] Power spectral entropy [90,111,121,134,138,157,175,176] Permutation entropy [134,157] Complexity and hand-crafted [76, 78, 80, 83, 84, 90, 115, 118, 120, 121, 123, 126, 128, 129, 132, 144, 146, 147, 149, 150, 163-165, 169, 174, 177, 179, 180, 184, 193, 197, 202, 204, 217, 220, 227, 239, 244, 255, 259, 326, 353] showed that the accuracy of each model varies according to the utilized feature, and SVM had the highest susceptibility compared to the other methods, while the probabilistic neural network (PNN) had a slightly lower accuracy. The performance of NNs is similar to SVM, and it has some advantages such as lower training time [145,180].…”

Section: Entropy-basedmentioning

confidence: 99%

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Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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“…Guo et al [15] successfully employed a long short-term memory network to analyse features extracted through wavelet packet decomposition of in-process acoustic emission signals for the assessment of grinding wheel condition. And to increase the robustness of chatter identification in thin-walled milling processes, an issue caused by highly dynamic and complex workpiece-tool interactions, Wang et al [16] developed an adaptive time-frequency domain feature selection process. In conjunction with a decision tree model, the authors were able to detect chatter with an accuracy of 92.42%.…”

mentioning

confidence: 99%

The application of machine learning to sensor signals for machine tool and process health assessment

Moore

Stammers

Dominguez-Caballero

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Due to the latest advancements in monitoring technologies, interest in the possibility of early-detection of quality issues in components has grown considerably in the manufacturing industry. However, implementation of such techniques has been limited outside of the research environment due to the more demanding scenarios posed by production environments. This paper proposes a method of assessing the health of a machining process and the machine tool itself by applying a range of machine learning (ML) techniques to sensor data. The aim of this work is not to provide complete diagnosis of a condition, but to provide a rapid indication that the machine tool or process has changed beyond acceptable limits; making for a more realistic solution for production environments. Prior research by the authors found good visibility of simulated failure modes in a number of machining operations and machine tool fingerprint routines, through the defined sensor suite. The current research set out to utilise this system, and streamline the test procedure to obtain a large dataset to test ML techniques upon. Various supervised and unsupervised ML techniques were implemented using a range of features extracted from the raw sensor signals, principal component analysis and continuous wavelet transform. The latter were classified using convolutional neural networks (CNN); both custom-made networks, and pre-trained networks through transfer learning. The detection and classification accuracies of the simulated failure modes across all classical ML and CNN techniques tested were promising, with all approaching 100% under certain conditions.

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Chatter identification in thin-wall milling using an adaptive variational mode decomposition method combined with the decision tree model

Cited by 9 publications

References 31 publications

Frequency monitoring analysis of milling edge chatter in composites using WPD for frequency band selection

Frequency monitoring analysis of milling edge chatter in composites using WPD for frequency band selection

Chatter detection in milling processes—a review on signal processing and condition classification

The application of machine learning to sensor signals for machine tool and process health assessment

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