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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.

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Metadaten
Name: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:Article
Language:English
Year of Publication:2020
Tag:Industry 4.0; IoT; process data; quality management
Volume:93
Pagenumber:6
First Page:622
Last Page:627
Dewey Decimal Classification:670 Industrielle und handwerkliche Fertigung
Open Access:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International