Using context data to improve the overall quality in process chains
- Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
| Author of HS Reutlingen | Lucke, Dominik |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-28111 |
| DOI: | https://doi.org/10.1016/j.procir.2020.04.110 |
| ISSN: | 2212-8271 |
| Erschienen in: | Procedia CIRP |
| Publisher: | Elsevier |
| Place of publication: | Amsterdam |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2020 |
| Tag: | Industry 4.0; IoT; process data; quality management |
| Volume: | 93 |
| Page Number: | 6 |
| First Page: | 622 |
| Last Page: | 627 |
| DDC classes: | 670 Industrielle und handwerkliche Fertigung |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
