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
Article, Bibliographie
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Biegel, Tobias ; Jourdan, Nicolas ; Hernandez, Carlos ; Cviko, Amir ; Metternich, Joachim |
| Type of entry: | Bibliographie |
| Title: | Deep learning for multivariate statistical in-process control in discrete manufacturing: A case study in a sheet metal forming process |
| Language: | English |
| Date: | 2022 |
| Publisher: | Elsevier B.V. |
| Journal or Publication Title: | Procedia CIRP |
| Volume of the journal: | 107 |
| DOI: | 10.1016/j.procir.2022.05.002 |
| 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. |
| Uncontrolled Keywords: | Deep Learning, Autoencoder, Multivariate Statistical Process Control, Discrete Manufacturing, Anomaly Detection |
| Divisions: | 16 Department of Mechanical Engineering 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) 16 Department of Mechanical Engineering > Institute of Production Technology and Machine Tools (PTW) > CiP Center for industrial Productivity |
| Date Deposited: | 06 Feb 2023 07:36 |
| Last Modified: | 07 Feb 2023 07:39 |
| PPN: | 504383477 |
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