A system for automated tool wear monitoring and classification using computer vision

Title data

Friedrich, Markus ; Gerber, Theresa ; Dumler, Jonas ; Döpper, Frank:
A system for automated tool wear monitoring and classification using computer vision.
In: Procedia CIRP. Vol. 118 (2023) . - pp. 425-430.
ISSN 2212-8271
DOI: https://doi.org/10.1016/j.procir.2023.06.073

Official URL:

Abstract in another language

This paper presents an approach for automated monitoring and classification of face milling tool wear using computer vision. A test setup with low-cost equipment for in-machine application is developed and used to generate an image dataset from worn and new tools. Different types of filters and segmentation techniques are applied and compared for image preprocessing. For wear detection, both classification and regression models using convolutional neural networks are evaluated. Best results were obtained with a combined model. This demonstrates that optical wear monitoring is feasible with low-cost equipment. However, potentials for improvement were identified in manual labeling and image quality.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Computer Vision; Convolutional Neural Networks; Tool Condition Monitoring; Milling Process
Institutions of the University:	Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Frank Döpper
Result of work at the UBT:	Yes
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited:	25 Jul 2023 06:05
Last Modified:	25 Jul 2023 13:47
URI:	https://eref.uni-bayreuth.de/id/eprint/86198