Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

Aerospace executives can now optimize manufacturing processes by leveraging artificial intelligence (AI) with high-performance computing (HPC) technologies and the digital thread. A digital thread follows the lifecycle of a product from design inception through engineering and product lifecycle management, to manufacturing instructions, supply chain management, and through to service events. You'll be able to enhance the aerospace design process to protect budgets, avoid static production rates, and nudge your business ahead of competitors. Even better, as aerospace design becomes more complex, AI can help keep your business ahead of the innovation curve. I recently chatted with a vice president of IT Infrastructure at a large manufacturing company and his message was very clear: HPC technologies that support modeling and simulation are very important to his business users.

With the rapid pace of technological growth, it's not always easy to imagine where digital transformation is taking the manufacturing sector, but one good way of doing this is to take a closer look at the "Digital Twin" concept within the industrial Internet of Things (IoT). As IoT connectivity provides the manufacturing sector with an increasing number of ways to access sensor-driven data locked in industrial machines and equipment, the need for data analysis, management, and control methods has also become more crucial. The amount of data collected from monitoring a smart factory is enormous, but if that data isn't aggregated and organized in a way that can support the decision-making process, then it's of no use. One method that's proving to be invaluable to engineering and customer service teams that are looking to leverage collected data is that of the "Digital Twin". Digital Twin is a virtual representation that matches the physical attributes of a "real world" factory, production line, product or component in real time, through the use of sensors, cameras, and other data collection techniques.