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Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion

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Abstract

Chatter is a kind of self-excited vibration that often occurs in the milling process of thin-walled parts, which has become the main factor restricting production efficiency and quality. Due to the occurrence of chatter, the signal becomes more complex and unstable. In order to realize milling chatter detection of thin-walled parts, the method of multi-sensor signal fusion is used. A chatter detection method based on variational mode decomposition (VMD) and nonlinear dimensionless index is proposed by analyzing the characteristics of signals in time–frequency domain. Firstly, a series of intrinsic mode function (IMF) components are obtained by decomposing force and acceleration signals with VMD. When chatter occurs, the energy is transferred to the chatter frequency band. Each IMF signal’s nonlinear energy entropy (EE) is extracted to construct the feature vector. A support vector machine chatter identification model based on multi-sensor signal fusion is established. To solve the problem of model incremental updating, supervised learning and unsupervised learning are combined to provide a method for chatter detection.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

EE:

Energy entropy

VMD:

Variational modal decomposition

IMF:

Intrinsic mode function

FFT:

Fast Fourier transform

SOM:

Self-organizing feature map

SVM:

Support vector machine

WPT:

Wavelet packet decomposition

EMD :

Empirical mode decomposition

RBF:

Radial basis kernel function

SLD:

Stability lobe diagram

IL-KM-SVM:

Incremental K-means and SVM

IL-KM:

Incremental K-means

IL-SVM :

Incremental SVM

BO:

Bayesian optimization

EK :

Envelope kurtosis

f(t):

The time series of the signal

u k(t):

The kth IMF of signal time series decomposition

A k(t):

The instantaneous amplitude of the kth IMF

\({\phi }_{k}(t)\) :

The phase of the kth IMF

\({\omega }_{k}(t)\) :

Instantaneous frequency

\(\delta (t)\) :

Unit pulse function

\(\otimes\) :

Convolution operation

\({\partial }_{t}\) :

Gradient operation

\(\lambda \left(t\right)\) :

Lagrange multiplication operator

L :

Lagrange multiplier

\(\alpha\) :

VMD penalty factor

K :

Decomposition level

\({\omega }_{k}^{n+1}\) :

Center frequency of each IMF

e :

Convergence error

n :

Number of iterations

\(\zeta\) :

Relative error

E0:

Total energy

E i :

Energy of each IMF component

P i :

Energy share of each IMF

H E :

Energy entropy of the signal decomposed by VMD

\({x}_{o}\left(t\right)\) :

Absolute value obtained by Hilbert transform

\(x\left(t\right)\) :

Original signal

F z :

Tooth passing frequency

Z :

Number of tool teeth

n s :

Spindle speed

a p :

Axial cutting depth

a e :

Radial cutting width

V f :

Feed rate

c :

SVM Penalty factor

g :

Core parameter

K t :

Tangential cutting force coefficient

K r :

Radial cutting force coefficient

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Funding

This research was supported by the National Natural Science Foundation of China (Grant Number 52175393).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Mingwei Zhao, Caixu Yue & Xianli Liu

Authors
  1. Mingwei Zhao
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  2. Caixu Yue
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  3. Xianli Liu
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Contributions

Mingwei Zhao has organized the project, analyzed and arranged data, and wrote the manuscript; Caixu Yue contributed the experiments and collected and analyzed data; Xianli Liu helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Caixu Yue.

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Zhao, M., Yue, C. & Liu, X. Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion. Int J Adv Manuf Technol 125, 3925–3941 (2023). https://doi.org/10.1007/s00170-023-10944-x

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  • DOI: https://doi.org/10.1007/s00170-023-10944-x

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