Zink, Robin ; Flick, Dominik ; Hermann, Christoph ; Thiede, Sebastian ; Weigold, Matthias (2024)
Data-based energy performance root cause analysis methodology.
In: Procedia CIRP, 130
doi: 10.1016/j.procir.2024.10.203
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The automotive industry aims to achieve continual improvement in energy performance to reduce emissions and costs. High complexity in manufacturing systems makes comprehensive analyses economically infeasible thus a root cause analysis (RCA) methodology is required to identify and quantify subsystems with a high energy saving potential. This allows targeted analyses and measures to be derived. For this purpose, a data-based benchmarking approach for identification and quantification of energy saving potential is developed. The methodology has proven to be suitable to derive significant energy savings in the validation based on a real use case of a globally operating car manufacturer.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Zink, Robin ; Flick, Dominik ; Hermann, Christoph ; Thiede, Sebastian ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Data-based energy performance root cause analysis methodology |
| Sprache: | Englisch |
| Publikationsjahr: | 2024 |
| Ort: | Amsterdam |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 130 |
| DOI: | 10.1016/j.procir.2024.10.203 |
| Kurzbeschreibung (Abstract): | The automotive industry aims to achieve continual improvement in energy performance to reduce emissions and costs. High complexity in manufacturing systems makes comprehensive analyses economically infeasible thus a root cause analysis (RCA) methodology is required to identify and quantify subsystems with a high energy saving potential. This allows targeted analyses and measures to be derived. For this purpose, a data-based benchmarking approach for identification and quantification of energy saving potential is developed. The methodology has proven to be suitable to derive significant energy savings in the validation based on a real use case of a globally operating car manufacturer. |
| Freie Schlagworte: | Artificial Intelligence, Energy Efficiency, factory, machine learning, manufacturing |
| 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: | 02 Dez 2024 14:52 |
| Letzte Änderung: | 02 Dez 2024 14:52 |
| PPN: | 524338590 |
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| Suche nach Titel in: | TUfind oder in Google |
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