Brockhaus, Benjamin ; Hoffmann, Felix ; Metternich, Joachim ; Weigold, Matthias
Hrsg.: Behrens, Bernd-Arno ; Brosius, Alexander ; Drossel, Welf-Guntram ; Hintze, Wolfgang ; Ihlenfeldt, Steffen ; Nyhuis, Peter (2021)
Predictive Maintenance for Flexible Protective Covers in Machine Tools.
doi: 10.1007/978-3-030-78424-9_20
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Predictive maintenance of machines promises their operators the possibility of avoiding unexpected machine defects and thus, of increasing the availability of their plants. High expectations of the potential of predictivemaintenance have drawn both users and suppliers of machines and systems to deal intensively with the subject. However, only a fewcomponent suppliers have established development efforts or a marketable offering of predictive maintenance solutions in their own product portfolio. Protective covers of machine tools are used to shield vulnerable machine components from harmful influences from the machining area. Currently, they are either replaced after damages are evident and the machine itself may already be damaged or periodically, which means wasted wear reserve. The use of predictive maintenance promises to solve this existing conflict of objectives while reducing unexpected downtime. This paper examines the development and implementation of an MLbased predictive maintenance solution for an existing protective cover. The implementation and testbed-operation showthe validity of the presented approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Herausgeber: | Behrens, Bernd-Arno ; Brosius, Alexander ; Drossel, Welf-Guntram ; Hintze, Wolfgang ; Ihlenfeldt, Steffen ; Nyhuis, Peter |
| Autor(en): | Brockhaus, Benjamin ; Hoffmann, Felix ; Metternich, Joachim ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Predictive Maintenance for Flexible Protective Covers in Machine Tools |
| Sprache: | Englisch |
| Publikationsjahr: | 20 September 2021 |
| Ort: | Berlin |
| Verlag: | Springer |
| Buchtitel: | Production at the Leading Edge of Technology - Proceedings of the 11th Congress of the German Academic Association for Production Technology (WGP), Dresden, September 2021 |
| Reihe: | Lecture Notes in Production Engineering |
| DOI: | 10.1007/978-3-030-78424-9_20 |
| Kurzbeschreibung (Abstract): | Predictive maintenance of machines promises their operators the possibility of avoiding unexpected machine defects and thus, of increasing the availability of their plants. High expectations of the potential of predictivemaintenance have drawn both users and suppliers of machines and systems to deal intensively with the subject. However, only a fewcomponent suppliers have established development efforts or a marketable offering of predictive maintenance solutions in their own product portfolio. Protective covers of machine tools are used to shield vulnerable machine components from harmful influences from the machining area. Currently, they are either replaced after damages are evident and the machine itself may already be damaged or periodically, which means wasted wear reserve. The use of predictive maintenance promises to solve this existing conflict of objectives while reducing unexpected downtime. This paper examines the development and implementation of an MLbased predictive maintenance solution for an existing protective cover. The implementation and testbed-operation showthe validity of the presented approach. |
| Freie Schlagworte: | Predictive maintenance, Machine learning, Machine tool |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > TEC Fertigungstechnologie |
| Hinterlegungsdatum: | 21 Sep 2021 05:54 |
| Letzte Änderung: | 06 Jan 2022 14:31 |
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