Improved machine learning based flank wear identification by incorporating 3D-height maps
Citation Link: https://doi.org/10.15480/882.17990
Publikationstyp
Conference Paper
Date Issued
2025-09
Sprache
English
TORE-DOI
Start Page
542
End Page
550
Citation
15th Congress of the German Academic Association for Production Technology, WGP 2025
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Springer
ISBN of container
978-3-032-19524-1
978-3-032-19523-4
Coated cutting tools are very important in the field of machining, but predicting the temporal evolution of tool wear is still a major challenge due to the high complexity of the wear process. Although machine learning (ML) shows promise in this respect, traditional supervised learning models rely on labour-intensive and error-prone manual measurement of tool wear to generate training data, which is a process that is time-consuming and inefficient. This paper uses a machine vision approach that automates the identification of flank wear on coated indexable inserts for turning applications and extends these techniques by integrating 3D tool surface data via height maps into the input. For this purpose, images of indexable inserts are taken during external longitudinal turning of C45 + N and X5CrNi18–10 using a focus variation microscope. This enables a height map of the wear area to be generated. Image annotation is performed on the 2D images to maintain comparability with traditional methods. Furthermore, the images and height maps are automatically cropped to focus on the flank wear region. This reduces the class imbalance and allows the model to maintain higher image resolution, despite the limited input. Three different U-Net models are trained on this dataset: two with height maps and one without. A comparison of the three models focusing on prediction accuracy and learning efficiency shows that including height maps improves prediction performance and reduces the required dataset size for comparable accuracy.
Subjects
Convolutional neural networks
Machine vision
Tool Wear detection
Turning
DDC Class
621.8: Machine Engineering
006.31: Machine Learning
Publication version
publishedVersion