Ziegenbein, Amina ; Metternich, Joachim (2021)
Data-Based Quality Analysis in Machining Production: Influence of Data Pre-Processing on the Results of Machine Learning Models.
In: Procedia CIRP, 104
doi: 10.1016/j.procir.2021.11.146
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Quality assurance as a non-value-adding process is constantly reviewed for cost optimisation and potential savings. In the pursuit of utilising advanced data analysis and machine learning methods to improve efficiency of quality assurance in machining processes there are several influencing factors severely impacting the performance and hence the value of said methods. Especially data preparation is a time consuming task requiring both domain and data expert knowledge and yielding various options for data preparation. In this paper, the impact of different input data sets for predicting part quality in a drilling process is investigated, using machine control data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Ziegenbein, Amina ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Data-Based Quality Analysis in Machining Production: Influence of Data Pre-Processing on the Results of Machine Learning Models |
| 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.2021.11.146 |
| Kurzbeschreibung (Abstract): | Quality assurance as a non-value-adding process is constantly reviewed for cost optimisation and potential savings. In the pursuit of utilising advanced data analysis and machine learning methods to improve efficiency of quality assurance in machining processes there are several influencing factors severely impacting the performance and hence the value of said methods. Especially data preparation is a time consuming task requiring both domain and data expert knowledge and yielding various options for data preparation. In this paper, the impact of different input data sets for predicting part quality in a drilling process is investigated, using machine control data. |
| Freie Schlagworte: | Drilling, Machine learning, Part Quality |
| 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 |
| Hinterlegungsdatum: | 26 Jan 2022 07:09 |
| Letzte Änderung: | 26 Jan 2022 07:09 |
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