AI Accelerates Model-Based Engineering
Recent industry webinars indicate AI is being used in Model-Based Systems Engineering (MBSE) to automate routine modeling tasks, generate validation test cases, and flag requirement inconsistencies. Early adopters report significant time savings and a reduction in human error in the development of large, complex system models.
- Natural Language Processing (NLP) is a key AI technology being integrated into MBSE, with applications in automatically analyzing textual requirements from documents to check for quality and consistency. This helps to formalize requirements within SysML models, reducing ambiguity early in the design process. - AI is being used to create and manage "digital twins," which are dynamic virtual replicas of physical aerospace systems. Companies like Airbus use this technology, connecting it to over 12,000 aircraft to feed real-time data to their virtual counterparts for predictive maintenance and operational optimization. - The emerging SAE standard ARP6983 is being developed to provide specific guidelines for the development and certification of aeronautical products that implement AI, addressing the unique verification and validation challenges of these systems. - A significant portion of aerospace leaders, 63%, are open to adopting AI tools for supply chain management, yet only 6% are currently doing so, indicating a gap between interest and implementation. - The U.S. Department of Defense has indicated that MBSE will be incorporated into future defense contracts, viewing it as a catalyst for developing more complex and resilient systems, and is encouraging the integration of AI and machine learning. - IBM's Engineering AI Hub includes a use-case discovery agent designed to interpret natural-language requirements and automatically generate corresponding model elements in modeling software like Rhapsody, supporting both SysML v1 and the newer SysML v2. - An AI-Augmented MBSE Maturity Model has been proposed, defining six levels of AI integration, from "Manual MBSE" (Level 0) to "Autonomous AI Engineering" (Level 5), where AI can own and execute engineering tasks. - AI platforms are being developed to reduce the reliance on physical testing by up to 70% through enhanced simulation and analysis, helping to shorten time-to-market and lower development costs for complex aerospace products.
