A cluster‐based framework for interface analysis in large‐scale aerospace systems. Issue 5 (24th June 2021)

• Record Type: Journal Article
• Authors: Goldschmid, Josh; Corns, Steven
• Is Part Of: Systems engineering. Volume 24:Issue 5(2021)
• Page Start: 339
• Page End: 351
• Publication Date: 2021-06-24
• Subjects: cluster -- community detection -- DSM -- interface analysis -- MBSE -- systems integration; Systems engineering -- Periodicals; 620.0011
• DOI: 10.1002/sys.21589
• Languages: English
• ISSNs: 1098-1241

Abstract

Abstract: This paper proposes a framework using a community maturity level metric to determine the integration readiness of interface elements in a particular network cluster. As a proof of concept this methodology is applied to an aircraft seating system to assess the readiness of complex interfaces before proceeding to full‐scale production and systems integration. A multi‐objective genetic algorithm, MOGA‐Net, is coupled with the Newman‐Girvan modularity metric as a clustering algorithm. This algorithm identifies system elements grouped by common interfaces, referred to as community clusters. The TRL and IRL values for these elements is then used to calculate an overall community maturity level. The achieved performance in these clusters is then compared to the target performance to determine overall maturity of the interfaces. This is compared to other system readiness metrics and interface readiness metrics as applied to the aircraft seating system and was found to be more consistent with subject matter expert evaluations during the critical design review. This gives a better representation of the true readiness of system interfaces before entering design reviews to reduce overall integration risks.