Strategic Model Based Systems Engineering (MBSE)

As modern systems grow in complexity, effective management of system architectures requires advanced methodologies. Model-Based Systems Engineering (MBSE), particularly through the Systems Modeling Language (SysML), provides a structured approach to handling large-scale system models. However, manual SysML processes remain time-intensive and error-prone. With rapid advancements in artificial intelligence (AI), there is growing interest in leveraging AI to automate SysML model generation and management, enhancing efficiency and scalability. This project explores the feasibility of integrating AI into MBSE, focusing on existing AI tools, techniques, and methodologies that support system engineering processes. Through a comprehensive review of AI advancements relevant to MBSE, the study examines AI’s potential in automating SysML tasks such as model generation, validation, and data handling. Additionally, a conceptual framework is proposed for an AI-integrated SysML tool, outlining its architecture and functionalities. The findings highlight AI’s transformative potential in MBSE by streamlining workflows, reducing human error, and improving scalability. This research contributes to the growing body of knowledge on AI-driven systems engineering, offering a foundation for future development and integration of AI tools in MBSE practices.