Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Bassiuny, A. M.; Li, Xiaoli

doi:10.1016/j.ijmachtools.2006.06.016

Cited by 61 publications

(30 citation statements)

References 19 publications

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“…a larger vanish moment, otherwise singularities could be overlooked. WT is sometimes used for signal de-noising before applying another signal processing technique [76,117,126]. It is based on the rule that wavelet expansions tend to concentrate data energy into a relatively small number of large coefficients.…”

Section: Wavelet Transformmentioning

confidence: 99%

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Advanced monitoring of machining operations

et al. 2010

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Section: Wavelet Transformmentioning

confidence: 99%

“…Bassiuny and Li [126] applied HHT analysis to detect end mill flute breakage via feed-motor current signals (Fig. 11a).…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Advanced monitoring of machining operations

et al. 2010

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“…Compared with traditional analysis methods, the EMD is intuitive, direct, posterior and adaptive. Due to these special properties, the EMD has been used to address many science and engineering problems [22,23].…”

Section: Feature Extraction Based On Empirical Mode Decomposition 21mentioning

confidence: 99%

On-line chatter detection using servo motor current signal in turning

Hong

2011

Sci. China Technol. Sci.

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Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter, a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique, the empirical mode decomposition (EMD), is used to decompose motor current signal into many intrinsic mode functions (IMF). Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore, the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine (SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal. chatter detection, current signal, empirical mode decomposition (EMD), support vector machine (SVM) Citation:Liu H Q, Chen Q H, Li B, et al. On-line chatter detection using servo motor current signal in turning.

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“…EMD has been used to decompose a signal into a set of completely data-adaptive basis functions called intrinsic mode functions (IMFs) [10][11][12]. Given a data x(t), it is decomposed into a number of IMFs, c i [13]:…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Cited by 61 publications

References 19 publications

Advanced monitoring of machining operations

Advanced monitoring of machining operations

On-line chatter detection using servo motor current signal in turning

Fault diagnosis of stamping process based on empirical mode decomposition and learning vector quantization

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