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Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing

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Abstract

Machine chatter is still an unresolved and challenging issue in the milling process, and developing an online chatter identification and process monitoring system towards smart manufacturing is an urgent requirement. In this paper, two indicators of chatter detection are investigated. One is the real-time variance of milling force signals in the time domain, and the other one is the wavelet energy ratio of acceleration signals based on wavelet packet decomposition in the frequency domain. Then, a novel classification concept for vibration condition, called slight chatter, is proposed and integrated successfully into the designed multi-classification support vector machine (SVM) model. Finally, a mapping model between image and chatter indicators is established via a distance threshold on the image. The multi-SVM model is trained by the results of three signals as an input. Experiment data and detection accuracy of the SVM model are verified in actual machining. The identification accuracy of 96.66% has proved that the proposed solution is feasible and effective. The presented method can be used to select optimized milling parameters to improve machining process stability and strengthen manufacturing system monitoring.

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Abbreviations

AE:

Acoustic emission

ACC:

Auto-correlation coefficient

ANN:

Artificial neural network

BPNN-GA:

Back propagation neural network-genetic algorithm

CIR:

Coarse-grained information rate

CCD:

Charge coupled device

CWT:

Continuous wavelet transform

DFT:

Discrete Fourier transform

DTF:

Disturbance transfer function

DWT:

Discrete wavelet transform

EA:

Envelope analysis

ECDS:

Eddy current displacement sensor

EE:

Energy entropy

ER-PSD:

Energy ratio of power spectral density

EMD:

Empirical mode decomposition

EMHPM:

Enhanced multistage homotopy perturbation method

EEMD:

Ensemble empirical mode decomposition

FCM:

Fuzzy classification method

FFT:

Fast Fourier transform

Ga index:

Arithmetic average of gray level

GLCM:

Gray-level co-occurrence matrix

HHT:

Hilbert-huang transform

LDE:

Linear differential equation

LGDE:

Local gradient direction estimation

MLP:

Multi-layer perceptron

OVMD:

Optimized variational mode decomposition

PE:

Permutation entropy

PFD:

Peak force density

PSD:

Power spectral density

PSE:

Power spectral entropy

RBF:

Radial basis function

RTVF:

Real-time variance of force

SA:

Synchronous averaging

SAA:

Simulated annealing algorithm

SAFT:

Short angular Fourier transform

SLD:

Stability lobe diagram

SST:

Synchro squeezing transform

STFT:

Short-time Fourier transform

SVD:

Singular value decomposition

SVM:

Support vector machine

UCL:

Upper confidence limit

VMD:

Variational mode decomposition

WPD:

Wavelet packet decomposition

WVT:

Wigner-ville transformation

WTMM:

Wavelet transform modulus maxima

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Acknowledgements

Authors acknowledge the support from the National Key R&D Program of China (Grant No. 2017YFE0101400), and also appreciate reviewers for their critical comments.

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Authors and Affiliations

  1. School of Mechanical Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Dong-Dong Li, Wei-Min Zhang, Feng Xue & Jürgen Fleischer

  2. Bosch Rexroth Endowed Chair for Automation & Electrification Solutions, Sino-German College for Postgraduate Study, Tongji University, Shanghai, 201804, People’s Republic of China

    Wei-Min Zhang

  3. China North Engine Research Institute, Tianjin, 300400, People’s Republic of China

    Yuan-Shi Li

  4. WBK Institute of Production Science, Karlsruhe Institute of Technology, Karlsruhe, 76131, Germany

    Jürgen Fleischer

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  1. Dong-Dong Li
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  2. Wei-Min Zhang
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  3. Yuan-Shi Li
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  4. Feng Xue
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  5. Jürgen Fleischer
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Correspondence to Dong-Dong Li.

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Li, DD., Zhang, WM., Li, YS. et al. Chatter identification of thin-walled parts for intelligent manufacturing based on multi-signal processing. Adv. Manuf. 9, 22–33 (2021). https://doi.org/10.1007/s40436-020-00299-x

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  • DOI: https://doi.org/10.1007/s40436-020-00299-x

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