Details
| Translated title of the contribution | Increasing Resilience in Virtual Quality Inspection. |
|---|---|
| Original language | German |
| Pages (from-to) | 279-283 |
| Number of pages | 5 |
| Journal | ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb |
| Volume | 119 |
| Issue number | 4 |
| Publication status | Published - 30 Mar 2024 |
Abstract
The scope of quality inspections of large milled components in the aviation industry represents a large proportion of non-value-adding activities and causes high time and cost expenditure. One approach to reducing such costs is to carry out virtual quality inspections by analysing relevant data streams. One challenge of virtual quality inspections is to ensure sufficient transferability of the prediction models under changing manufacturing conditions. To investigate the resilience of virtual quality inspections, an approach for continuous learning of data-based prediction models was therefore developed. The results showed that the quality level could be correctly classified for about 91 % of the unknown data.
ASJC Scopus subject areas
- Engineering(all)
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
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In: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Vol. 119, No. 4, 30.03.2024, p. 279-283.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Steigerung der Resilienz in der virtuellen Qualitätsprüfung
AU - Denkena, Berend
AU - Wichmann, Marcel
AU - Reuter, Leon
AU - Böttcher, Alexander
N1 - Publisher Copyright: © 2024 Walter de Gruyter GmbH, Berlin/Boston, Germany.
PY - 2024/3/30
Y1 - 2024/3/30
N2 - The scope of quality inspections of large milled components in the aviation industry represents a large proportion of non-value-adding activities and causes high time and cost expenditure. One approach to reducing such costs is to carry out virtual quality inspections by analysing relevant data streams. One challenge of virtual quality inspections is to ensure sufficient transferability of the prediction models under changing manufacturing conditions. To investigate the resilience of virtual quality inspections, an approach for continuous learning of data-based prediction models was therefore developed. The results showed that the quality level could be correctly classified for about 91 % of the unknown data.
AB - The scope of quality inspections of large milled components in the aviation industry represents a large proportion of non-value-adding activities and causes high time and cost expenditure. One approach to reducing such costs is to carry out virtual quality inspections by analysing relevant data streams. One challenge of virtual quality inspections is to ensure sufficient transferability of the prediction models under changing manufacturing conditions. To investigate the resilience of virtual quality inspections, an approach for continuous learning of data-based prediction models was therefore developed. The results showed that the quality level could be correctly classified for about 91 % of the unknown data.
KW - Adaptivity
KW - Industry 4.0
KW - Quality Management
KW - Resilience
KW - Virtual Quality Inspection
UR - http://www.scopus.com/inward/record.url?scp=85193203322&partnerID=8YFLogxK
U2 - 10.1515/zwf-2024-1036
DO - 10.1515/zwf-2024-1036
M3 - Artikel
AN - SCOPUS:85193203322
VL - 119
SP - 279
EP - 283
JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
SN - 0947-0085
IS - 4
ER -