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

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.

Graphical abstract

Keywords

tool condition monitoring / cutting temperature / neural network / learning rate adaption

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Nomenclature

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
Variables
c	Light velocity
C1, C2	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
λ1, λ2	Two different wavelengths

Acknowledgements

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

RIGHTS & PERMISSIONS

2022 Higher Education Press 2022