Improving quality prediction in radial-axial ring rolling using a semi-supervised approach and generative adversarial networks for synthetic data generation
- As artificial intelligence and especially machine learning gained a lot of attention during the last few years, methods and models have been improving and are becoming easily applicable. This possibility was used to develop a quality prediction system using supervised machine learning methods in form of time series classification models to predict ovality in radial-axial ring rolling. Different preprocessing steps and model implementations have been used to improve quality prediction. A semi-supervised approach is used to improve the prediction and analyze, to what extend it can improve current research in machine learning for quality prediciton. Moreover, first research steps are taken towards a synthetic data generation within the radial-axial ring rolling domain using generative adversarial networks.
| Author: | Simon FahleORCiDGND, Thomas GlaserGND, Andreas KneißlerGND, Bernd KuhlenkötterORCiDGND |
|---|---|
| URN: | urn:nbn:de:hbz:294-100767 |
| DOI: | https://doi.org/10.1007/s11740-021-01075-x |
| Parent Title (English): | Production engineering |
| Publisher: | Springer |
| Place of publication: | Berlin |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2023/08/31 |
| Date of first Publication: | 2021/09/07 |
| Publishing Institution: | Ruhr-Universität Bochum, Universitätsbibliothek |
| Tag: | GAN; Machine learning; Radial-axial ring rolling; Semi-supervised; Time series classifcation |
| Volume: | 16 |
| First Page: | 175 |
| Last Page: | 185 |
| Note: | Dieser Beitrag ist auf Grund des DEAL-Springer-Vertrages frei zugänglich. |
| Institutes/Facilities: | Lehrstuhl für Produktionssysteme |
| Dewey Decimal Classification: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften, Maschinenbau |
| open_access (DINI-Set): | open_access |
| faculties: | Fakultät für Maschinenbau |
| Licence (English): |  Creative Commons - CC BY 4.0 - Attribution 4.0 International |
