Abstract:
The work performed for the Adaptive Resilient Engineered Structures (ARES) program sponsored by the U.S. Army constitutes a trade study and resulting proposal for a structural demonstrator platform. The trade study was conducted using the Quality Function Deployment (QFD) process and a subsequent Artificial Intelligence (AI) exercise to find clusters of technologies for structural efficiency and resilience from Boeing's internal research activities. From a selection of approximately 150 technologies at different TRLs, Boeing subject matter experts (SMEs) for structural technologies identified several characteristics that could potentially determine the development of ARES structural demonstrator. Through the QFD process, the list of technologies was down selected about 50 unique technologies for consideration. The next stage of the QFD process entailed in identifying 37 different attributes or criteria long which each of these technologies would be assessed. They were grouped under two different categories: vehicle performance criteria and program performance criteria. Importance scores were provided by the SMEs independently and then a statistical approach for AI was used to distill them to 9 significant ones (labeled as 'Pillars') and a further distillation to 3 significant features (labeled as 'Super Metrics'). Clustering algorithms were then employed to group the set of technologies that could provide the resiliency targets sought for the demonstrator platform. The clusters were compared a hypothetical ideal platform to determine suitability and finally, 12 technologies merited attention toward the stated goals of the demonstrator platform.