Jourdan, Nicolas ; Biegel, Tobias ; Knauthe, Volker ; von Buelow, Max ; Guthe, Stefan ; Metternich, Joachim (2021)
A computer vision system for saw blade condition monitoring.
54th CIRP Conference on Manufacturing Systems. virtual Conference (22.-24.09.2021)
doi: 10.1016/j.procir.2021.11.186
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Jourdan, Nicolas ; Biegel, Tobias ; Knauthe, Volker ; von Buelow, Max ; Guthe, Stefan ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | A computer vision system for saw blade condition monitoring |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Verlag: | Elsevier |
| Reihe: | Procedia CIRP |
| Band einer Reihe: | 104 |
| Veranstaltungstitel: | 54th CIRP Conference on Manufacturing Systems |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 22.-24.09.2021 |
| DOI: | 10.1016/j.procir.2021.11.186 |
| Kurzbeschreibung (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. |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 18 Jul 2022 08:25 |
| Letzte Änderung: | 18 Jul 2022 08:25 |
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