Biegel, Tobias ; Jourdan, Nicolas ; Hernandez, Carlos ; Cviko, Amir ; Metternich, Joachim (2022)
Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process.
In: Procedia CIRP, 107
doi: 10.1016/j.procir.2022.05.002
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing Multivariate Statistical Process Control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling's T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Biegel, Tobias ; Jourdan, Nicolas ; Hernandez, Carlos ; Cviko, Amir ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 107 |
| DOI: | 10.1016/j.procir.2022.05.002 |
| Kurzbeschreibung (Abstract): | Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing Multivariate Statistical Process Control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling's T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results. |
| Freie Schlagworte: | Deep Learning, Autoencoder, Multivariate Statistical Process Control, Discrete Manufacturing, Anomaly Detection |
| 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) > CiP Center für industrielle Produktivität |
| Hinterlegungsdatum: | 06 Feb 2023 07:36 |
| Letzte Änderung: | 07 Feb 2023 07:39 |
| PPN: | 504383477 |
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