Ziegenbein, Amina ; Fertig, Alexander ; Metternich, Joachim ; Weigold, Matthias (2020)
Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process.
In: Procedia CIRP, 93
doi: 10.1016/j.procir.2020.03.063
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
The rush of new providers of Industrial Internet of Things solutions and machine learning applications onto the market opens up new possibilities for data acquisition and analysis that go beyond the classical approach of model- and empirical-based process analysis. In this context, classic production tasks, e.g. quality assurance by random sampling, should be critically reviewed for their relevance. These non-value-adding activities can potentially be eliminated by disruptive digitalisation in order to increase labour productivity. This paper showcases the potential of a data-driven approach for quality determination in a drilling process using machine control data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Ziegenbein, Amina ; Fertig, Alexander ; Metternich, Joachim ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Data-Based Process Analysis in Machining Production: Case Study for Quality Determination in a Drilling Process |
| Sprache: | Englisch |
| Publikationsjahr: | 29 September 2020 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 93 |
| DOI: | 10.1016/j.procir.2020.03.063 |
| URL / URN: | https://www.sciencedirect.com/science/article/pii/S221282712... |
| Kurzbeschreibung (Abstract): | The rush of new providers of Industrial Internet of Things solutions and machine learning applications onto the market opens up new possibilities for data acquisition and analysis that go beyond the classical approach of model- and empirical-based process analysis. In this context, classic production tasks, e.g. quality assurance by random sampling, should be critically reviewed for their relevance. These non-value-adding activities can potentially be eliminated by disruptive digitalisation in order to increase labour productivity. This paper showcases the potential of a data-driven approach for quality determination in a drilling process using machine control data. |
| Freie Schlagworte: | Quality, Machine Learning, Drilling |
| 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) > Zerspanungstechnologie (2021 aufgegangen in TEC Fertigungstechnologie) |
| Hinterlegungsdatum: | 08 Okt 2020 06:06 |
| Letzte Änderung: | 08 Okt 2020 06:06 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum