  Tool flank wear monitoring can minimize machining downtime costs while
increasing productivity and product quality. In some industrial applications,
only a limited level of tool wear is allowed to attain necessary tolerances. It
may become challenging to monitor a limited level of tool wear in the data
collected from the machine due to the other components, such as the flexible
vibrations of the machine, dominating the measurement signals. In this study, a
tool wear monitoring technique to predict limited levels of tool wear from the
spindle motor current and dynamometer measurements is presented. High-frequency
spindle motor current data is collected with an industrial edge device while
the cutting forces and torque are measured with a rotary dynamometer in
drilling tests for a selected number of holes. Feature engineering is conducted
to identify the statistical features of the measurement signals that are most
sensitive to small changes in tool wear. A neural network based on the long
short-term memory (LSTM) architecture is developed to predict tool flank wear
from the measured spindle motor current and dynamometer signals. It is
demonstrated that the proposed technique predicts tool flank wear with good
accuracy and high computational efficiency. The proposed technique can easily
be implemented in an industrial edge device as a real-time predictive
maintenance application to minimize the costs due to manufacturing downtime and
tool underuse or overuse.
