A cluster‐based framework for interface analysis in large‐scale aerospace systems

• Published in: Systems engineering Vol. 24; no. 5; pp. 339 - 351
• Main Authors: Goldschmid, Josh, Corns, Steven
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.09.2021
• Subjects: Aerospace systems; Aircraft; Algorithms; cluster; Clustering; community detection; DSM; Genetic algorithms; Interface analysis; Interfaces; MBSE; Modularity; Systems integration
• ISSN: 1098-1241; 1520-6858
• DOI: 10.1002/sys.21589

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.