Assessing visual identification challenges for unmarked and similar aircraft components

Publikationstyp
Conference Paper
Publikationsdatum
2022-06
Sprache
English
Author
Schoepflin, Daniel orcid-logo
Gierecker, Johann orcid-logo
Schüppstuhl, Thorsten orcid-logo
Institut
Flugzeug-Produktionstechnik M-23 
DOI
10.15480/882.4902
TORE-URI
http://hdl.handle.net/11420/14499
Start Page
135
End Page
145
Citation
31st International Conference on Flexible Automation and Intelligent Manufacturing (FAIM 2022)
Contribution to Conference
31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022 
Publisher DOI
10.1007/978-3-031-18326-3_14
Scopus ID
2-s2.0-85141827303
Publisher
Springer
Highest demands for complete traceability and quality control of each component, require thorough identification of each produced, replaced, and (dis-)assembled aircraft component. As many production and MRO-processes for modern aircraft remain to be carried out manually, this poses a great challenge. Many small components either do not feature a Part Number or in MRO-processes their Part Number is occluded or not readable due to dirt and wear. Considering unmarked components with a high resemblance to one another and few characteristics, e.g. standard parts such as bushings and pipes, manual identification is an error-prone task. Avoiding errors through digitalized procedures has the potential to significantly reduce error rates and costs for a typical manual dual control. However, automated identification of components has to overcome the high classification complexity that originates in the manifold of aircraft components and is additionally increased by individualistic MRO modifications for specific aircraft. This work presents a methodological approach to reveal possible challenges for identification procedures and gives special focus to the assessment of similarities between components. Two similarity metrics are introduced that are calculated either through feature-based analysis or through 3D-shape similarity assessment. The methodology is demonstrated with two to this date unsolved Use-Cases that represent different challenges of visual identification systems for similar and unmarked components.
Schlagworte
Identification challenges
Object classification
Similarity of objects
Visual sensor applications
DDC Class
600: Technik
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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