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Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning

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Abstract

Tool failures in machining processes often cause severe damages of workpieces and lead to large quantities of loss, making tool condition monitoring an important, urgent issue. However, problems such as practicability still remain in actual machining. Here, a real-time tool condition monitoring method integrated in an in situ fiber optic temperature measuring apparatus is proposed. A thermal simulation is conducted to investigate how the fluctuating cutting heats affect the measuring temperatures, and an intermittent cutting experiment is carried out, verifying that the apparatus can capture the rapid but slight temperature undulations. Fourier transform is carried out. The spectrum features are then selected and input into the artificial neural network for classification, and a caution is given if the tool is worn. A learning rate adaption algorithm is introduced, greatly reducing the dependence on initial parameters, making training convenient and flexible. The accuracy stays 90% and higher in variable argument processes. Furthermore, an application program with a graphical user interface is constructed to present real-time results, confirming the practicality.

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Abbreviations

AE:

Acoustic emission

ANN:

Artificial neural network

CNN:

Convolutional neural network

FFT:

Fast Fourier transform

SOM:

Self-organizing feature map

SVM:

Support vector machine

TFTC:

Thin film thermocouple

c :

Light velocity

C 1, C 2 :

Constants that contain values such as the Planck constant and the Boltzmann constant

e:

Elementary charge

h :

Planck constant

k :

Boltzmann constant

L :

Radiation intensity

N :

Number of categories

p :

Output vector

R :

Ratio of the two radiation intensities

T :

Temperature

ε :

Wavelength-dependent emissivity

λ :

Wavelength

λ 1, λ 2 :

Two different wavelengths

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Acknowledgements

The authors acknowledge the financial support from the Key-Area Research and Development Program of Guangdong Province, China (Grant No. 2020B090927002).

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Kaiwei Cao, Jinghui Han, Tielin Shi, Guanglan Liao & Zhiyong Liu

  2. Foxconn Industrial Internet Co., Ltd., Shenzhen, 518109, China

    Long Xu

Authors
  1. Kaiwei Cao
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  2. Jinghui Han
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  3. Long Xu
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  4. Tielin Shi
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  5. Guanglan Liao
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  6. Zhiyong Liu
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Correspondence to Zhiyong Liu.

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Cao, K., Han, J., Xu, L. et al. Real-time tool condition monitoring method based on in situ temperature measurement and artificial neural network in turning. Front. Mech. Eng. 17, 5 (2022). https://doi.org/10.1007/s11465-021-0661-3

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  • DOI: https://doi.org/10.1007/s11465-021-0661-3

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