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.

翻译：刀具磨损监测对于制造过程中的质量控制和成本降低至关重要，钻削加工即为其中一例。本文提出了一种基于U-Net的语义图像分割管道，应用于切削刀片的显微图像，以实现磨损检测。磨损区域可分为两种不同类型，从而构成一个多类分类问题。另一方面，将两种磨损类型合并为一个通用磨损类别，则可将问题表述为二元分类任务。除了二元与多类问题的比较外，本文还研究了不同的损失函数，即交叉熵（Cross Entropy）、焦点交叉熵（Focal Cross Entropy）以及基于交并比（IoU）的损失函数。此外，模型在不同尺寸的图像瓦片上进行了训练，并采用了不同强度的数据增强技术。研究发现，性能最佳的模型是二元模型，其在适度增强的数据上训练，并使用了基于IoU的损失函数。