at Google ScholarTitle 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
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 Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology Faculties > Faculty of Engineering Science > Chair Manufacturing and Remanufacturing Technology > Chair Manufacturing and Remanufacturing Technology - Univ.-Prof. Dr.-Ing. Frank Döpper Research Institutions > Central research institutes > Research Center for AI in Science and Society Research Institutions Research Institutions > Central research institutes |
| 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: | 06 Nov 2025 07:33 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/86198 |