Control of High Speed Milling Chatter in Simultaneous Time-Frequency Domain

2011 ◽  
Author(s):  
Meng-Kun Liu ◽  
C. Steve Suh
Keyword(s):  
Frequency Domain ◽  
Dynamic Stability ◽  
High Speed ◽  
Adaptive Controller ◽  
System Response ◽  
Discrete Wavelet ◽  
Line System ◽  
High Speed Milling ◽  
Time Frequency ◽  
Milling Chatter

The dynamics governing high speed milling has been extensively explored for decades, both numerically and experimentally. The process loses its dynamic stability and becomes chaotic through either Neimark-Sacker or period doubling bifurcation. While its associated response is still bounded in the time domain, however, it could become unstably broadband in the frequency domain, thus causing the uneven cutting surface on the workpiece and tool damage. A discrete-wavelet-based feedforward adaptive controller is developed to stabilize system response in the time and frequency domains simultaneously. An adaptive controller along with an adaptive filter effective for on-line system identification is implemented in the wavelet domain. By controlling both time and frequency responses, the presented controller design is demonstrated to effectively suppress milling chatter and restore the system back to dynamic stability.

Mitigation of Milling Chatter at High-Speed

2012 ◽  
Author(s):  
Meng-Kun Liu ◽  
C. Steve Suh
Keyword(s):  
High Speed ◽  
Stability Boundary ◽  
Adaptive Controller ◽  
Machining Process ◽  
Discrete Wavelet ◽  
Line System ◽  
Milling Stability ◽  
Tool Vibrations ◽  
The Time Domain ◽  
Milling Chatter

A highly interrupted machining process, milling at high speed can be dynamically unstable and chattering with aberrational tool vibrations. While its associated response is still bounded in the time domain, however, milling could become unstably broadband and chaotic in the frequency domain, inadvertently causing poor tolerance, substandard surface finish and tool damage. Instantaneous frequency along with marginal spectrum is employed to investigate the route-to-chaos process of a nonlinear, time-delayed milling model. It is shown that marginal spectra are the tool of choice over Fourier spectra in identifying milling stability boundary. A novel discrete-wavelet-based adaptive controller is explored to stabilize the nonlinear response of the milling tool in the time and frequency domains simultaneously. As a powerful feature, an adaptive controller along with an adaptive filter effective for on-line system identification is implemented in the wavelet domain. By exerting proper mitigation schemes to both the time and frequency responses, the controller is demonstrated to effectively deny milling chatter and restore milling stability as a limit cycle of extremely low tool vibrations.

A Method of Tool Breakage Detection in CNC High-Speed Milling

2013 ◽  
Vol 779-780 ◽  
pp. 384-389
Author(s):  
Qi Gao
Keyword(s):  
High Speed ◽  
Tool Condition Monitoring ◽  
Monitoring Systems ◽  
Discrete Wavelet ◽  
Fast Method ◽  
High Speed Machining ◽  
Tool Breakage ◽  
High Speed Milling ◽  
Motor Current ◽  
Tool Condition

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition.

Correlation between Cutting Forces and Tool Wear in High Speed Milling of Graphite

2009 ◽  
Vol 69-70 ◽  
pp. 403-407 ◽  
Author(s):  
Li Zhou ◽  
Cheng Yong Wang ◽  
Xiao Jun Wang ◽  
Zhe Qin
Keyword(s):  
Tool Wear ◽  
Cutting Force ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Tools ◽  
Dynamic Force ◽  
High Speed Milling ◽  
Time Frequency ◽  
Abrasive Friction ◽  
Force Components

Cutting tools suffer severe abrasive friction and wear in high speed milling of graphite. Cutting forces were measured and analyzed using time-frequency analysis method to reveal the correlation between cutting force variations and tool wear evolution. The static and dynamic force components increased prominently with tool wear. The cutting force Fy was found the most sensitive to the tool wear evolution. The waveform of cutting force became periodic and irregular with the increase of tool wear. Good correlation was found between the first force harmonic and tool wear.

scholarly journals Dynamic Responses of the Metro Train’s Bogie Frames: Field Tests and Data Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xuhui He ◽  
Kehui Yu ◽  
Chenzhi Cai ◽  
Yunfeng Zou
Keyword(s):  
Frequency Domain ◽  
Dynamic Characteristics ◽  
Field Tests ◽  
Dynamic Responses ◽  
Discrete Wavelet ◽  
Bogie Frame ◽  
Time Frequency ◽  
Acceleration Signal ◽  
Track Irregularity ◽  
Acceleration Signals

This paper focuses on the dynamic characteristics of the metro train’s bogie frames based on the field test data. The acceleration signals of both motor bogie frame and trailer bogie frame of a standard B-type metro train were measured. Running tests on the Metro line 21 of Guangzhou (China) were carried out. The acquired acceleration signals of bogie frames were analyzed through several methods to identify the dynamic characteristics of the motor and trailer bogies in the time-frequency domain. The spectral analysis and time-frequency representations show that noise components exist in the high-frequency domain of the original signal, especially for the acceleration signal of the motor bogie frame. Then, the soft thresholding process and discrete wavelet transform decomposition process are conducted to obtain a denoised version of the original signals in the time-frequency domain. The vibration frequency domain and energy distribution of bogie frames under different train speeds are analyzed. The track irregularity wavelength of the metro line is calculated and analyzed based on the measured bogie frames’ acceleration signals. The dynamic characteristics of the metro train’s bogie frames in this paper can be adopted as a reference in the track diagnosis of the elevated metro line.

scholarly journals Characterization of Wavelet Decomposition Strain Signal Using the K-Mean Clustering Method

2018 ◽  
Vol 7 (3.17) ◽  
pp. 158
Author(s):  
A A. Rahim ◽  
C H. Chin ◽  
S Abdullah ◽  
S S. K. Singh ◽  
M Z. Nuawi ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Frequency Domain ◽  
Root Mean Square ◽  
Time Domain ◽  
Average Distance ◽  
Industrial Area ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Time Frequency ◽  
Strain Signal

This paper aims to study the characterisation of time-frequency domain to analyse the fatigue strain signal due to weaknesses in time domain and frequency domain approaches. The objectives were to determine the behaviour of strain signal, characterise the fatigue life of strain signal and validate the fatigue life in time-frequency domain. The strain signal was obtained using data acquisition devices and strain gauges on two types of road condition including highway and industrial area. The acquired signals were analysed with time domain, frequency domain and time-frequency domain approaches. In time-frequency domain, the signals were decomposed using 4th Daubechies discrete wavelet transform. To validate the effectiveness of time-frequency approach in characterising vibration fatigue signal, fatigue data was clustered by mapping of the data based on the spectrum energy, root-mean-square and fatigue life obtained. The clustering was performed by comparing the centroid values which both data had five clusters as the optimum data clustering with 0.836 average distance to centroid. From this, the relationship between fatigue life, root-mean-square and spectrum energy can be determined and thus a new fatigue life criterion was developed. 

scholarly journals A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Adrielle C. Santana ◽  
Adriano V. Barbosa ◽  
Hani C. Yehia ◽  
Rafael Laboissière
Keyword(s):  
Sample Size ◽  
Frequency Domain ◽  
High Dimension ◽  
Cross Validation ◽  
Signal To Noise Ratio ◽  
Event Related Potentials ◽  
Discrete Wavelet ◽  
Prediction Errors ◽  
Time Frequency ◽  
Related Potentials

Abstract Background A common problem in neurophysiological signal processing is the extraction of meaningful information from high dimension, low sample size data (HDLSS). We present RoLDSIS (regression on low-dimension spanned input space), a regression technique based on dimensionality reduction that constrains the solution to the subspace spanned by the available observations. This avoids regularization parameters in the regression procedure, as needed in shrinkage regression methods. Results We applied RoLDSIS to the EEG data collected in a phonemic identification experiment. In the experiment, morphed syllables in the continuum /da/–/ta/ were presented as acoustic stimuli to the participants and the event-related potentials (ERP) were recorded and then represented as a set of features in the time-frequency domain via the discrete wavelet transform. Each set of stimuli was chosen from a preliminary identification task executed by the participant. Physical and psychophysical attributes were associated to each stimulus. RoLDSIS was then used to infer the neurophysiological axes, in the feature space, associated with each attribute. We show that these axes can be reliably estimated and that their separation is correlated with the individual strength of phonemic categorization. The results provided by RoLDSIS are interpretable in the time-frequency domain and may be used to infer the neurophysiological correlates of phonemic categorization. A comparison with commonly used regularized regression techniques was carried out by cross-validation. Conclusion The prediction errors obtained by RoLDSIS are comparable to those obtained with Ridge Regression and smaller than those obtained with LASSO and SPLS. However, RoLDSIS achieves this without the need for cross-validation, a procedure that requires the extraction of a large amount of observations from the data and, consequently, a decreased signal-to-noise ratio when averaging trials. We show that, even though RoLDSIS is a simple technique, it is suitable for the processing and interpretation of neurophysiological signals.

scholarly journals Analysis of WOFDM over LTE 1.25 MHz Band

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Sandeep Sarowa ◽  
Naresh Kumar ◽  
Ram Sewak Singh
Keyword(s):  
Wireless Communication ◽  
Orthogonal Frequency Division Multiplexing ◽  
High Speed ◽  
Discrete Wavelet ◽  
Bandwidth Utilization ◽  
Error Performance ◽  
Time Frequency ◽  
The One ◽  
Achievable Data Rate

Orthogonal Frequency Division Multiplexing (OFDM) is the one of the most preferred multiplexing technique for realizing high-speed wireless communication, like Long Term Evolution (LTE) and LTE-Adv. In the era of digital wireless communication, applications of wavelet theory have been favorably applied in many areas of signal processing. Orthogonality, flexible time-frequency analysis, and the ability to characterize signals accurately have attracted the attention of the telecommunication community to use wavelet as a basis function for OFDM. In this paper, discrete wavelet transform (DWT) has been proposed as an alternative signal analysis with multiple merits such as support high-speed applications, immune to distortion, wavelet diversity, better error performance, and efficient bandwidth utilization. A simulative analysis of various wavelets, at different modulation techniques, over OFDM has been presented to demonstrate the improvement in BER performance. Further, in accordance with the LTE parameterization over 1.25 MHz band, the performance of wavelet-based OFDM (WOFDM) is found significantly higher in terms of maximum achievable data rate and system spectral efficiency.

scholarly journals Time Frequency Analysis of Wavelet and Fourier Transform

2020 ◽  
Author(s):  
Karlton Wirsing
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Frequency Domain ◽  
Frequency Analysis ◽  
Discrete Wavelet ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Efficient Access ◽  
The Fourier Transform

Signal processing has long been dominated by the Fourier transform. However, there is an alternate transform that has gained popularity recently and that is the wavelet transform. The wavelet transform has a long history starting in 1910 when Alfred Haar created it as an alternative to the Fourier transform. In 1940 Norman Ricker created the first continuous wavelet and proposed the term wavelet. Work in the field has proceeded in fits and starts across many different disciplines, until the 1990’s when the discrete wavelet transform was developed by Ingrid Daubechies. While the Fourier transform creates a representation of the signal in the frequency domain, the wavelet transform creates a representation of the signal in both the time and frequency domain, thereby allowing efficient access of localized information about the signal.

Sign in / Sign up

Export Citation Format

Share Document