  Tool wear monitoring is crucial for quality control and cost reduction in
manufacturing processes, of which drilling applications are one example. In
this paper, we present a U-Net based semantic image segmentation pipeline,
deployed on microscopy images of cutting inserts, for the purpose of wear
detection. The wear area is differentiated in two different types, resulting in
a multiclass classification problem. Joining the two wear types in one general
wear class, on the other hand, allows the problem to be formulated as a binary
classification task. Apart from the comparison of the binary and multiclass
problem, also different loss functions, i. e., Cross Entropy, Focal Cross
Entropy, and a loss based on the Intersection over Union (IoU), are
investigated. Furthermore, models are trained on image tiles of different
sizes, and augmentation techniques of varying intensities are deployed. We
find, that the best performing models are binary models, trained on data with
moderate augmentation and an IoU-based loss function.
