Recently, Industry 4.0 has
become a trend for manufacturing industries, and under the
core concept of the machine tools industry will be gradually
evolved from pure automation to smart factories. The key
issue resides in the monitoring of cutting tools conditions;
consequently, the timing of the cutting tools changes.
However, literatures on tapping are limited. Tapping is
quite a complicated process, which is an unstable process
and often accompanies with chip clogging, tool breakage, bad
thread quality, and other failures. Moreover, since tapping
is near the finishing process, the associated repair work is
very time-consuming and laborious when problems should
occur. If workpiece cannot be repaired, previous efforts may
come in vain. Previous research on tapping shows torque
signals were collected by dynamometers. However,
dynamometers are expensive and not practical to be applied
in production environments. In this study, torque command
signals directly acquired from CNC controller are applied
with a use of convolutional neural network. The model
established is shown to distinguish healthy tool from the
worn tool.
In the study, various
approaches were used to convert torque command signals to
images, which were then imported in convolutional neural
network for model training. Convolutional neural network is
particularly powerful in recognizing images and voices. In
particular, the improvement of hardware and algorithms
dramatically speeds up computing process.
By means of the above
approach, best timing to change taps can be determined and
the time required to check threads can be drastically
reduced.
Keywords: convolutional neural
network, tapping tool condition, timing of changing tools,
spindle torque command, thread quality inspection