Chatter Frequency Identification and Amplitude Tracking Using Short-Time Difference Spectrum Analysis

Xu, Xiaoqiang; Zhou, Tianyu; Hu, Hongwei; Hu, Yongle

doi:10.1109/tim.2020.3003361

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…High-speed milling is characterized not only by a high material removal rate, but also by a low cutting force [7][8][9][10], small deformation [11], easy penetration of the mechanism due to the kinematic sharpening of the cutting wedge [12,13], high quality processing and is widely used in production ensuring the high perfamance of production [14][15][16]. In fast machining with cutting speed detection, the chips are deformed too late and flow out of the rake face.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided design system for the end mill made by laser ablation and grinding

Pivkin,

Yazev,

Grechishnikov

et al. 2023

Advanced Laser Processing and Manufacturing VII

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In designing a conical end mill with a helical front surface, the geometry of the grinding wheel has an influence on the shape of the back profile, as this study shows. For the first time, the influence of the generix of a conical circle's angle of inclination on various interference schemes has been studied. Through the combination of laser ablation and grinding, the underpoints of the helical surface were given their final shapes, indicating the standard sizes that would affect the execution's ability to be manufactured. This work will result in the engineering of a system for automated manufacturing of conical mills, as well as the development of control programs for CNC grinding and laser ablation machines. It will also enable the development of design support for cutting tool production. The work reveals analytical dependences of the value of the clearance angle, controlled at the point of the flank surface during the transition from the radius of the cutting edge to the profile section of the helical flute in the radial section, which is set from the coordinate of a parametrically defined point along the OX axis in the radial section. It has been established that the value of the radius of curvature of the cutting edge and the point of formation of the transition of the radius to the profile can significantly change the kinematic geometry of the cutter (up to nine degrees when the radius of the cutting-edge changes by 30 μmm).

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Section: Introductionmentioning

confidence: 99%

Computer-aided design system for the end mill made by laser ablation and grinding

Pivkin,

Yazev,

Grechishnikov

et al. 2023

Advanced Laser Processing and Manufacturing VII

View full text Add to dashboard Cite

show abstract

“…The information in both time and frequency domains can be combined together to achieve better results. To study the signal in time and frequency domain simultaneously, Some researchers used Time -Frequency analysis such as Short-Time Fourier Transform (STFT) (Uekita and Takaya 2017;Xu et al 2020), Continuous Wavelet Transform (CWT) (Lee et al 2017;Tran et al 2020), Wavelet Transform (WT) (Berger et al 1998;Yoon and Chin 2005) and Wigner-Ville Distribution (WVD) (Cai et al 2019).…”

Section: Introductionmentioning

confidence: 99%

SBLMD–ANN–MOPSO-based hybrid approach for determining optimum parameter in CNC milling

Mishra

Singh

2023

Soft Comput

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In the recent decades, researchers have proposed various techniques to mitigate chatter effects in milling operation. Still a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been processed using a modified Spline Based Local Mean Decomposition (SBLMD) technique in order to extract tool chatter features. Further, six artificial neural network (ANN) training algorithms viz.Resilient Propagation (RP), Conjugate Gradient Based (CGP and SCG), Quasi-Newton Based (BFGS and OSS) and Levenberg-Marquardt Algorithm (LM) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms, suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, multi-objective particle swarm optimization (MOPSO) has been applied to optimize developed prediction models for obtaining an optimal range of input parameters pertaining to stable milling with higher productivity.

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“…Chatter identification and feature extraction is the second stage. To study the signal in the time and spectral domains simultaneously, researchers used methods such as spectrograms [11], synchrosqueezing transforms [12], wavelet transforms [13] and Wigner-Ville distributions [14]. However, due to the imperatives of the Heisenberg uncertainty rule, time-Frequency methods are not able to extract features.…”

Section: Introductionmentioning

confidence: 99%

A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes

Mishra

Singh

2022

Meas. Sci. Technol.

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In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.

show abstract

Chatter Frequency Identification and Amplitude Tracking Using Short-Time Difference Spectrum Analysis

Cited by 13 publications

References 31 publications

Computer-aided design system for the end mill made by laser ablation and grinding

Computer-aided design system for the end mill made by laser ablation and grinding

SBLMD–ANN–MOPSO-based hybrid approach for determining optimum parameter in CNC milling

A novel ensemble method based on the SBLMD-ANN-MOPSO approach for predicting milling stability regimes

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