Predicting production times through machine learning for scheduling additive manufacturing orders in a PPC system
- Additive manufacturing (AM) is a promising manufacturing method for many industrial sectors. For this application, industrial requirements such as high production volumes and coordinated implementation must be taken into account. These tasks of the internal handling of production facilities are carried out by the Production Planning and Control (PPC) information system. A key factor in the planning and scheduling is the exact calculation of manufacturing times. For this purpose we investigate the use of Machine Learning (ML) for the prediction of manufacturing times of AM facilities.
| Author of HS Reutlingen | Baumung, Wjatscheslav |
|---|---|
| DOI: | https://doi.org/10.1109/ICIASE45644.2019.9074152 |
| ISBN: | 978-1-5386-8139-8 |
| Erschienen in: | Intelligent applied systems on engineering : proceedings of 2019 IEEE International Conference of Intelligent Applied Systems on Engineering (IEEE ICIASE 2019) : April 26-29, Fuzhou, Fujian, China |
| Publisher: | IEEE |
| Place of publication: | Piscataway, NJ |
| Document Type: | Conference proceeding |
| Language: | English |
| Publication year: | 2019 |
| Tag: | AM; ML; PPC |
| Page Number: | 4 |
| First Page: | 47 |
| Last Page: | 50 |
| DDC classes: | 004 Informatik |
| Open access?: | Nein |
| Licence (German): |  In Copyright - Urheberrechtlich geschützt |
