  As an integral part of contemporary manufacturing, monitoring systems obtain
valuable information during machining to oversee the condition of both the
process and the machine. Recently, diverse algorithms have been employed to
detect tool wear using single or multiple sources of measurements. In this
study, a deep learning approach is proposed for estimating tool wear,
considering cutting parameters. The model's accuracy and transferability in
tool wear estimation were assessed with milling experiments conducted under
varying cutting parameters. The results indicate that the proposed method
outperforms conventional methods in terms of both transferability and rapid
learning capabilities.
