[go: up one dir, main page]

Cao et al., 2013 - Google Patents

Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform

Cao et al., 2013

Document ID
6390930233510135896
Author
Cao H
Lei Y
He Z
Publication year
Publication venue
International Journal of Machine Tools and Manufacture

External Links

Snippet

Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/46Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis

Similar Documents

Publication Publication Date Title
Cao et al. Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform
Cao et al. Chatter identification in end milling process based on EEMD and nonlinear dimensionless indicators
Schmitz Chatter recognition by a statistical evaluation of the synchronously sampled audio signal
Shrivastava et al. A comparative study of EMD and EEMD approaches for identifying chatter frequency in CNC turning
Ji et al. Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation
Li et al. A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting
Bassiuny et al. Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator
Liu et al. On-line chatter detection in milling using fast kurtogram and frequency band power
Ji et al. EEMD-based online milling chatter detection by fractal dimension and power spectral entropy
Benkedjouh et al. Tool wear condition monitoring based on continuous wavelet transform and blind source separation
Lamraoui et al. Cyclostationarity approach for monitoring chatter and tool wear in high speed milling
Yu et al. Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis
Schmitz et al. Exploring once-per-revolution audio signal variance as a chatter indicator
Liu et al. Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy
Kalvoda et al. A cutter tool monitoring in machining process using Hilbert–Huang transform
Li et al. Online chatter detection in milling process based on VMD and multiscale entropy
Pechenin et al. Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling
Rabi et al. Analysis of vibration signal responses on pre induced tunnel defects in friction stir welding using wavelet transform and empirical mode decomposition
Chen et al. Chatter detection in milling processes using frequency-domain Rényi entropy
Xiao et al. A novel approach to machining condition monitoring of deep hole boring
Raja et al. Hilbert–Huang transform-based emitted sound signal analysis for tool flank wear monitoring
Yuan et al. Ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection
Matthew et al. Improved STFT analysis using time-frequency masking for chatter detection in the milling process
He et al. Rolling bearing localized defect evaluation by multiscale signature via empirical mode decomposition
Shrivastava et al. Estimation of stable cutting zone in turning based on empirical mode decomposition and statistical approach