Schibsdat, SebastianSebastianSchibsdatDege, Jan H.Jan H.Dege2026-08-192026-08-192025-0915th Congress of the German Academic Association for Production Technology, WGP 2025https://hdl.handle.net/11420/64423Coated 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.enhttps://creativecommons.org/licenses/by/4.0/Convolutional neural networksMachine visionTool Wear detectionTurningTechnology::621: Applied Physics::621.8: Machine EngineeringComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningConference Paper10.1007/978-3-032-19524-1_5810.15480/882.17990