In recent years, the
manufacturing industry has paid more attention to the
development of models for predicting mechanical system
failure or tool life. In past tool life prediction model in
milling was established in a physics-based manner. However,
for complex manufacturing systems, it is quite difficult to
build a suitable mathematical model so predictive models
based on data analysis are being developed.
In this study, the spindle
current and the vibration signal captured by the controller
and the accelerometers placed on the vise are used as the
corresponding features of the tool wear state. The spindle
current is the signal source of the machine itself, so no
additional sensors are needed. This signal is highly
positively correlated with tool wear. Accelerometers are
small, low cost, and a high signal-to-noise ratio, which can
monitor the change of tool status more instantaneously than
the cutting force. This study utilizes the above features to
establish a machine learning model to identify the state of
tool wear, then compare the identification results of the
artificial neural network with the random forest model, and
discuss the physical meaning behind the important features.
The recognition rate of the random forest model is 10%-20%
higher than that of the neural network.
Keywords: vibration signal,
accelerometers, tool wear, machine learning, milling