Jourdan, Nicolas ; Biegel, Tobias ; Knauthe, Volker ; Buelow, Max von ; Guthe, Stefan ; Metternich, Joachim (2022):
A computer vision system for saw blade condition monitoring. (Publisher's Version)
In: Procedia CIRP, 104, pp. 1107-1112. Elsevier, ISSN 2212-8271,
DOI: 10.26083/tuprints-00021265,
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Item Type: | Article |
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Origin: | Secondary publication |
Status: | Publisher's Version |
Title: | A computer vision system for saw blade condition monitoring |
Language: | English |
Abstract: | Tool condition monitoring is a key component of predictive maintenance in smart manufacturing. Predicting excessive tool wear in machining processes becomes increasingly difficult if different materials need to be processed. We propose a novel computer vision-based system for saw blade condition monitoring that is independent of the processed materials and combines deep learning with classic computer vision. Our approach allows for accurate condition monitoring of blade wear which can further be used for predictive maintenance. Additionally, the system can classify different defect types such as missing blade teeth, thus preventing the production of scrap parts. |
Journal or Publication Title: | Procedia CIRP |
Volume of the journal: | 104 |
Place of Publication: | Darmstadt |
Publisher: | Elsevier |
Classification DDC: | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 670 Industrielle und handwerkliche Fertigung |
Divisions: | 20 Department of Computer Science > Interactive Graphics Systems |
Date Deposited: | 06 May 2022 10:25 |
Last Modified: | 03 Mar 2023 10:10 |
DOI: | 10.26083/tuprints-00021265 |
Corresponding Links: | |
URN: | urn:nbn:de:tuda-tuprints-212658 |
Additional Information: | Erscheint auch in: CIRP CMS 2021 - 54th CIRP Conference on Manufacturing Systems, 2021 |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/21265 |
PPN: | 505422700 |
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