Online Failure Detection in Face Milling Using Discrete Wavelet Transform

Kasashima, Nagayoshi; Mori, Kazuo; Ruíz, Gilberto Herrera; Taniguchi, Nobuyuki

doi:10.1016/s0007-8506(07)62368-3

Cited by 50 publications

(38 citation statements)

References 5 publications

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“…But the usual analysis cannot be applied to detect if a tool breaks. In this paper, the Morlet wavelet function is used to deal with the spindle motor current signals, given by (9) Its continuous wavelet is (10) In this equation, the relation between the parameters and is (11) where corresponds to the frequency of the wavelet function; sample frequency of the signals; frequency focused on the energy of the signals. When a tool is breaking, the feed motor current signals show some spikes [ Fig.…”

Section: B Tool Breakage Detection By Using Wavelet Transformsmentioning

confidence: 99%

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Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Tso

Wang

2000

IEEE Trans. Syst., Man, Cybern. C

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Abstract-In this paper, wavelet transforms and fuzzy techniques are used to monitor tool breakage and wear conditions in real time according to the measured spindle and feed motor currents, respectively. First, the continuous and discrete wavelet transforms are used to decompose the spindle and feed ac servo motor current signals to extract signal features so as to detect the breakage of drills successfully. Next, the models of the relationships between the current signals and the cutting parameters are established under different tool wear states. Subsequently, fuzzy classification methods are used to detect tool wear states based on the above models. Finally, the two methods above are integrated to establish an intelligent tool condition monitoring system for drilling operations. The monitoring system can detect tool breakage and tool wear conditions using very simple current sensors. Experimental results show that the proposed system can reliably detect tool conditions in drilling operations in real time and is viable for industrial applications.

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Section: B Tool Breakage Detection By Using Wavelet Transformsmentioning

confidence: 99%

“…Zhou et al [10] used neural networks and wavelet techniques to propose a new tool-life criterion. Kasashima et al [11] described an application of the discrete wavelet transform (DWT) to detecting tool failures in face milling operations. Tansel et al [12] applied wavelet transform to compress cutting force signal.…”

Section: Introductionmentioning

confidence: 99%

Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Tso

Wang

2000

IEEE Trans. Syst., Man, Cybern. C

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“…It has been used to analyze tool breakage monitoring signals [9,10]. DWT has a good solution simultaneously in both frequency and time domains so that it can extract more information in the time domain at different frequency bands [11]. This paper presents a DWT application for real-time tool breakage monitoring in drilling.…”

Section: Introductionmentioning

confidence: 99%

Discrete wavelet transform for tool breakage monitoring

Dong

Yuan

1999

International Journal of Machine Tools and Manufacture

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Detection of tool breakage is of vital importance in automated manufacturing. Various methods have been attempted, and it is considered that the use of discrete wavelet transform (DWT), which is much more efficient and just as accurate wavelet analysis, may provide a realistic solution to the detection of tool breakage in operation. The DWT uses an analyzing wavelet function which is localized in both time and frequency to detect a small change in the input signals. In addition, it requires less computation than Fast Fourier Transformation (FFT). This paper discusses a tool breakage monitoring system based on DWT of an acoustic emission (AE) and an electric feed current signal using an effective algorithm. The experiment results show overall 98.5% reliability and the good real-time monitoring capability of the proposed methodology for detecting tool breakage during drilling.

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“…It has been used to analyze tool breakage monitoring signal [8,9]. WT has a good solution in the timefrequency domain so that it can extract more information in the time domain at different frequency bands [10].…”

Section: Introductionmentioning

confidence: 99%

On-line detection of the breakage of small diameter drills using current signature wavelet transform

1999

International Journal of Machine Tools and Manufacture

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This paper presents on-line tool breakage detection of small diameter drills by monitoring the AC servo motor current. The continuous wavelet transform was used to decompose the spindle AC servo motor current signal and the discrete wavelet transform was used to decompose the feed AC servo motor current signal in time-frequency domain. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent on-line capability; in addition, it had a low sensitivity to change of the cutting conditions and high success rate for the detection of the breakage of small diameter drills.

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Online Failure Detection in Face Milling Using Discrete Wavelet Transform

Cited by 50 publications

References 5 publications

Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Real-time tool condition monitoring using wavelet transforms and fuzzy techniques

Discrete wavelet transform for tool breakage monitoring

On-line detection of the breakage of small diameter drills using current signature wavelet transform

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