F2D-11496-SF241-D023 - Automated MBSE Model Generation of Space Systems

The United States Space Force (USSF) is committed to enhancing the application of standard Systems Engineering (SE) processes among technical professionals involved in the identification, acquisition, testing, and maintenance of military weapon systems. The USSF's objective of "repeatable excellence" in this field is challenged by the growing complexity and interdependence of these systems, where SE best practices are dispersed across various entities, including acquirers, integrators, and other organizations within the supply chain. In response, the USSF adheres to the System Engineering Assessment Model (SEAM), specifically tailored for use in USSF programs and projects. SEAM is designed to ensure that systems are effectively designed, tested, and integrated prior to deployment, utilizing a disciplined approach throughout the product lifecycle.  At the Air Force Research Laboratory (AFRL) Space Vehicles Directorate (AFRL/RVE), located at Kirtland Air Force Base, Systems Engineers are responsible for overseeing the adequacy of SE processes, ensuring compliance, and fostering Continuous Process Improvement. This involves the manual verification of document-based artifacts and reporting to Division Leaders. However, this manual methodology for evaluating system design artifacts and work products against SEAM requirements is labor-intensive and lacks the consistency needed for repeatable in-depth analysis.  To address these inefficiencies and align with the rapid pace of technological advancement, the USSF recognizes the necessity of an automated and intelligent validation process to significantly enhance program performance. Our proposed solution involves the integration of Model-Based Systems Engineering (MBSE), Artificial Intelligence (AI), Machine Learning (ML), and data fusion technologies. MBSE offers an organized approach to modeling systems, employing open and established standards that streamline the artifact collection and validation process. AI's role is crucial in automating model checking and reasoning, thereby reducing the reliance on manual validation and enabling Systems Engineers to focus on other critical design aspects. Furthermore, data fusion adds an extra layer of validation, corroborating models with real-world data, which increases confidence in the system models and design.  This integration of MBSE with AI, ML, and data fusion technologies represents a significant advancement in the USSF's Systems Engineering approach, promising greater efficiency, accuracy, and reliability in the development and maintenance of complex military weapon systems.