Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools

  • In order to ensure high productivity and quality in industrial production, early identification of tool wear is needed. Within the context of Industry 4.0, we integrate wear monitoring of solid carbide milling and drilling cutters automatically into the production process. Therefore, we propose to analyze wear types with image instance segmentation using Mask R-CNN with feature pyramid and bounding box regression. Our approach is able to recognize the five most important wear types: flank wear, crater wear, fracture, built-up edge and plastic deformation. While other methods use image classification and classify only one wear type for each image, our model is able to detect multiple wear types. Over 35 models with different hyperparameter settings were trained on 5,000 labeled images to establish a reliable classifier. The results show up to 82.03% accuracy and benefit for overlapping wear types, which is crucial for using the model in production.

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Metadaten
Author:Jasmin DalferthORCiD, Sven WinkelmannORCiD, Friedhelm SchwenkerORCiD
DOI:https://doi.org/10.1007/978-3-030-58309-5_18
ISBN:9783030583088
ISSN:0302-9743
Parent Title (English):Artificial Neural Networks in Pattern Recognition
Publisher:Springer International Publishing
Place of publication:Cham
Document Type:Part of a Book
Language:English
Date of first Publication:2020/09/02
Release Date:2024/08/09
Pagenumber:12
First Page:223
Last Page:234
institutes:Fakultät für Elektrotechnik Feinwerktechnik Informationstechnik
Research Themes:Digitalisierung & Künstliche Intelligenz
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