Petruschke, Lars ; Walther, Jessica ; Burkhardt, Max ; Luther, Max ; Weigold, Matthias (2021)
Machine Learning Based Identification of Energy States of Metal Cutting Machine Tools Using Load Profiles.
In: Procedia CIRP, 104
doi: 10.1016/j.procir.2021.11.060
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
In order to quantify energy efficiency potentials of metal cutting machine tools, it is necessary to determine the time shares of different energy states. This paper presents a machine learning approach analyzing energy states, developed according to the acrfullCRISPDM, to improve the accuracy of the time study compared to static approaches. Different concepts, such as Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), are deployed and evaluated based on electrical load profiles from an industrial use case with 35 metal cutting machine tools, where both approaches achieve an accuracy of over 95 %.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Petruschke, Lars ; Walther, Jessica ; Burkhardt, Max ; Luther, Max ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning Based Identification of Energy States of Metal Cutting Machine Tools Using Load Profiles |
| Sprache: | Englisch |
| Publikationsjahr: | 26 November 2021 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 104 |
| DOI: | 10.1016/j.procir.2021.11.060 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Kurzbeschreibung (Abstract): | In order to quantify energy efficiency potentials of metal cutting machine tools, it is necessary to determine the time shares of different energy states. This paper presents a machine learning approach analyzing energy states, developed according to the acrfullCRISPDM, to improve the accuracy of the time study compared to static approaches. Different concepts, such as Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM), are deployed and evaluated based on electrical load profiles from an industrial use case with 35 metal cutting machine tools, where both approaches achieve an accuracy of over 95 %. |
| Freie Schlagworte: | Machine tool, energy states, convolutional neural network, long short-term memory |
| 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) > ETA Energietechnologien und Anwendungen in der Produktion |
| Hinterlegungsdatum: | 08 Dez 2021 07:11 |
| Letzte Änderung: | 08 Dez 2021 07:11 |
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