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.

本文介绍了一种基于 U-Net 的语义图像分割管道，用于监测切削刀具在钻孔应用中的磨损情况，并将问题以二进制分类任务的形式加以解决。研究还考察了不同的损失函数，以及使用不同强度的图像增强技术训练图像。我们发现，最好的表现模型是使用 IoU 损失函数，在中度增强数据上训练的二进制模型。