In order to analyze various process characteristics, grinding simulations can be used, which need accurate models of the tool and the individual grains. For this purpose, grinding tools can be digitized. To identify characteristic grains from a large number of measurements, each individual grain has to be analyzed and separated from the bond manually. Therefore, a deep learning-based methodology was developed to achieve a high segmentation accuracy of the grain boundaries efficiently. Additionally, a data augmentation approach was investigated to limit the data necessary for learning. The model transferability was quantified by analyzing different states of tool wear.

中文翻译：

---

基于深度学习的磨具数字化增强语义分割

为了分析各种工艺特性，可以使用磨削模拟，这需要刀具和单个晶粒的准确模型。为此，可以将磨削工具数字化。为了从大量测量中识别特征颗粒，必须手动分析每个单独的颗粒并将其与键分离。因此，开发了一种基于深度学习的方法来有效地实现晶界的高分割精度。此外，研究了一种数据增强方法来限制学习所需的数据。通过分析刀具磨损的不同状态来量化模型的可转移性。