Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation

Ji, Yongjian; Wang, Xibin; Liu, Zhibing; Wang, Hongjun; Li, Jiao; Wang, Dongqian; Leng, Shouyang

doi:10.1016/j.jsv.2018.07.019

Cited by 63 publications

(28 citation statements)

References 47 publications

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“…The commonly-used complexity measure of signals is fractal analysis. Ji et al (2018) applied a single-fractal analysis for chatter detection, assuming scale invariance of signals independent on time. However, temporal variations in scale invariant structure often appear (Ihlen, 2012).…”

Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

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Chatter in machining results in poor workpiece surface quality and short tool life. A reliable chatter detection method is needed to monitor this self-excited vibration before its complete development. This paper applies multifractal features extracted from a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism is able to discover a complex singular behavior rising on unstable chatter signals, and improves chatter detection performance. The multifractal features are selected using a multivariate filter, and assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features cannot only detect chatter with high accuracy and short computation time, but are also efficient to distinguish stable tests with large amplitudes from unstable tests. A small p is recommended to highlight dynamic variations and obtain high classification accuracies. For further verification, the

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Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

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“…In several papers, these features are ranked and are utilized as the input for the machine learning classifiers. 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.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to WPT, EMD and EEMD are also often utilized to featurize cutting signals. Ji et al [23] proposed EMD to both eliminate noise from milling vibration signals and to extract features from informative Intrinsic Mode Functions (IMF). Chen et al [24] used top-ranked features extracted from the IMFs obtained from EEMD for machining state detection.…”

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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“…The standard approach for chatter recognition from cutting signals has mostly focused on extracting features by decomposing the time series and combining them with supervised learning algorithms-most commonly Support Vector Machine (SVM). The two most widely used decompositions are Wavelet Packet Transform (WPT) [6][7][8][9][10][11][12][13][14] and Ensemble Empirical Mode Decomposition (EEMD) [14][15][16][17][18]. Both WPT and EEMD require manually preprocessing the signal to identify the most informative parts of the signal which carry chatter signatures, which is characterized by the part of the decomposition whose spectrum contain the chatter frequency.…”

Section: Introductionmentioning

confidence: 99%

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

Yesilli

Tymochko

Khasawneh

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Chatter detection has become a prominent subject of interest due to its effect on cutting tool life, surface finish and spindle of machine tool. Most of the existing methods in chatter detection literature are based on signal processing and signal decomposition. In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process. Persistence diagrams, a method of representing topological features, are not easily used in the context of machine learning, so they must be transformed into a form that is more amenable. Specifically, we will focus on two different methods for featurizing persistence diagrams, Carlsson coordinates and template functions. In this paper, we provide classification results for simulated data from various cutting configurations, including upmilling and downmilling, in addition to the same data with some added noise. Our results show that Carlsson Coordinates and Template Functions yield accuracies as high as 96% and 95%, respectively. We also provide evidence that these topological methods are noise robust descriptors for chatter detection.

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Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation

Cited by 63 publications

References 47 publications

Chatter detection for milling using novel p-leader multifractal features

Chatter detection for milling using novel p-leader multifractal features

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

Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

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