Improved machine learning based flank wear identification by incorporating 3D-height maps

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

Funding(s)

SPP 2402: Extrapolationsfähige digitale Greybox-Modelle zur Beschreibung und Vorhersage des makroskopischen Systemverhaltens TiAlN-beschichteter Zerspanwerkzeuge

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

978-3-032-19524-1_58.pdf

Type

Main Article

Size

2.12 MB

Format

Adobe PDF

Options

Improved machine learning based flank wear identification by incorporating 3D-height maps