  Tool wear conditions impact the final quality of the workpiece. In this
study, we propose a deep learning approach based on a convolutional neural
network that incorporates cutting conditions as extra model inputs, aiming to
improve tool wear estimation accuracy and fulfill industrial demands for
zero-shot transferability. Through a series of milling experiments under
various cutting parameters, we evaluate the model's performance in terms of
tool wear estimation accuracy and its transferability to new fixed or variable
cutting parameters. The results consistently highlight our approach's advantage
over conventional models that omit cutting conditions, maintaining superior
performance irrespective of the stability of the wear development or the
limitation of the training dataset. This finding underscores its potential
applicability in industrial scenarios.
