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Towards an Intelligent Digital Cabin Twin to Support an Aircraft's Retrofit and Base Maintenance (SAE Paper 2022-01-0046)
Technical Paper
2022-01-0046
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Event:
AeroTech® Digital Summit
AeroTech
Language:
English
Abstract
Aircraft are high value-adding and long-living assets, while aircraft cabins are expensive consumer products tailored to each customer. Vastly changing requirements and needs force aircraft holders regularly to instruct modifications in order to remain attractive on the market. Adaptations, modifications, and development of innovations are handled by multiple organizations, not by a central one like the aircraft’s manufacturer or owner. Although the Continuing Airworthiness Management Organization manages all aircraft instance-specific documents as required by aviation regulations, their format and types of management differ. Besides, not all information that arises during a parts design phase is included. That means, overall, the consistent model-based maintenance of data within all phases of PLM up to disposal is not guaranteed. The loss of information during all lifecycle phases can cause the prolongation of planning phases and the actual ground-time during retrofit processes since further data acquisitions, processing, and linkage to determine an as-is state are necessary. Employing a Digital Twin can minimize the effort to accumulate, manage, and provide the consistent model-based cabin’s as-is state. This work proposes an integrated concept to maintain and associate 3D/2D as-is, as-designed, descriptive, as-well-as document-based data that form a digital representation of the real aircraft’s cabin and airframe structure it is interfacing. Storing and providing the data as a digital replica does not create substantial added value yet. Therefore, we present a concept that qualifies the Digital Twin’s intelligent part through an automatic generation of descriptive data and semantics. Eventually, we propose valuable applications that will significantly enhance planning phases based on the twin’s output.
Authors
- Keno H. W. Moenck - Hamburg University of Technology
- Fabian N. Laukotka - Hamburg University of Technology
- Constantin Deneke - Hamburg University of Technology
- Thorsten Schüppstuhl - Hamburg University of Technology
- Dieter Krause - Hamburg University of Technology
- Thorsten J. Nagel - Lufthansa Technik AG
Topic
Citation
Moenck, K., Laukotka, F., Deneke, C., Schüppstuhl, T. et al., "Towards an Intelligent Digital Cabin Twin to Support an Aircraft's Retrofit and Base Maintenance (SAE Paper 2022-01-0046)," SAE Technical Paper 2022-01-0046, 2022, https://doi.org/10.4271/2022-01-0046.Also In
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