Due to the increasing computing power and corresponding algorithms, the use of machine learning (ML) in production technology has risen sharply in the age of Industry
4.0. Data availability in particular is fundamental at this point and a prerequisite for the successful implementation of a ML application. If the quantity or quality of data is
insufficient for a given problem, techniques such as data augmentation, the use of synthetic data and transfer learning of similar data sets can provide a remedy. In this paper,
the concept of transfer learning is applied in the field of radial-axial ring rolling (rarr) and implemented using the example of time series prediction of the outer diameter
over the process time. Radial-axial ring rolling is a hot forming process and is used for seamless ring production.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:88341 |
| Date | 28 November 2023 |
| Creators | Seitz, Johannes, Wang, Qinwen, Moser, Tobias, Brosius, Alexander, Kuhlenkötter, Bernd |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 18, urn:nbn:de:bsz:14-qucosa2-882872, qucosa:88287, urn:nbn:de:bsz:14-qucosa2-883403, qucosa:88340 |
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