Chatter detection in band sawing based on discriminant analysis of sound features

Thaler, Tilen; Potočnik, Primož; Bric, Ivan; Govekar, Edvard

doi:10.1016/j.apacoust.2012.12.004

Cited by 72 publications

(26 citation statements)

References 34 publications

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“…Power spectrum characteristics in the frequency domain [3][4][5][6][7] and the wavelet transform [8] of various sensory signals are most often use for the experimental characterization of stability and related chatter during cutting processes. However, the drawback of using power spectrum or wavelet based characterization is that the informative frequency components depend on the modal parameters of the mechanical system being tested.…”

Section: Introductionmentioning

confidence: 99%

Stability diagrams and chatter avoidance in horizontal band sawing

2015

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Section: Introductionmentioning

confidence: 99%

Stability diagrams and chatter avoidance in horizontal band sawing

2015

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“…Support Vector Machine (SVM) algorithm is the most common classifier used for chatter classification [10,23,24,25,26,27,28]. Other less common classifiers include quadratic discrimination analysis [29], Hidden Markov Model (HMM) [30], generalized HMM [31], and logistic regression [32] (cf. Fig.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to WPT and EMD-based approaches, there are other methods for feature extraction from metal removal processes. For example, Thaler et al [29] used Short-Time Fourier Transform to extract the frequency domain features of the feed force, acceleration, and sound pressure signals in band sawing operation. Moreover, the Q-factor and the power spectrum of the signal were used for chatter classification in milling [27].…”

Section: Introductionmentioning

confidence: 99%

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

Yesilli

Khasawneh

Otto

2020

CIRP Journal of Manufacturing Science and Technology

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The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with Support Vector Machine (SVM) classifier combined with Recursive Feature Elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as 94% and 91%, respectively, for WPT and EEMD. However, EEMD is shown to outperform WPT in transfer learning applications with accuracy of up to 84%. Therefore, for systems where the movement of the cutting center leads to significant variations in the stiffness of the machine-tool system, we recommend using EEMD over WPT for training a classifier. This is because EEMD retains higher accuracy rates in comparison to WPT when the input data stream deviates from the data that was used to train the classifier.

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“…There are other methods devoted to the applications of online chatter detection, such as chatter identification based on wavelet transform (WT) and support vector machine (SVM), (11) Hilber-Huang transform (HHT), (12) and short-time Fourier transform (STFT). (13,14) The time-frequency signal processing tools, such as STFT, WT, HHT, and Teager-Huang transform (THT), are gradually applied to dynamic signal analyses owing to their superior performance in both time and frequency domains simultaneously. The signal-resolving powers of STFT and WT are limited by the time window function and the mother wavelet type, respectively.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Signal Analysis by Teager–Huang Transform for Milling Chatter Recognition

Lee¹,

Yu²,

Wang³

et al. 2020

Sensors and Materials

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Many types of sensor have been utilized to monitor milling vibration, and many analysis methods are devoted to the investigation of milling vibration or milling dynamics. In this work, a noncontact sensor and a time-frequency domain analysis method were applied to identify the state of milling vibration. A microphone was employed in practical tests to record the milling dynamics. The Teager-Huang transform (THT) was adopted for the acoustic signal analysis owing to its high resolution in the time-frequency domain. The potential frequency range for the analysis of milling dynamics is reported in this work to improve the recognition accuracy of milling vibration limited by the effect of environmental noise. The THT was used to distinguish the chatter state from the normal milling dynamics. In addition, the statistical index called the coefficient of variation was applied to define the threshold of chatter occurrence. Milling experiments (including dry and wet cuttings) were performed to verify the proposed chatter detection method.

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Chatter detection in band sawing based on discriminant analysis of sound features

Cited by 72 publications

References 34 publications

Stability diagrams and chatter avoidance in horizontal band sawing

Stability diagrams and chatter avoidance in horizontal band sawing

On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition

Acoustic Signal Analysis by Teager–Huang Transform for Milling Chatter Recognition

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