This work presents the design and development of aArtificial Intelligence (AI)n AI-powered tool aimed at streamlining model-based systems engineeringSystems engineering (MBSEModel Based Systems Engineering (MBSE)) workflows. The tool converts natural language inputs into MBSEModel Based Systems Engineering (MBSE) models by combining large language modelsLarge Language Models (LLM) and natural language processing techniques with MBSEModel Based Systems Engineering (MBSE) software APIs. This work also proposes a potential framework for evaluating LLMLarge Language Models (LLM)-generated models, focusing on metrics such as accuracy, completeness, consistency, clarity, and usability. Integrating generative AIGenerative AI into systems engineering processesSystems engineering process is highly effective for automating routine tasks, boosting productivity, and supporting the ongoing digital transformationDigital transformation in the field.

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Design and Development of an AI-Enabled Systems Engineering Model Generation Tool

  • Jyotirmay Gadewadikar,
  • Tomi Esho,
  • Jeremy Marshall

摘要

This work presents the design and development of aArtificial Intelligence (AI)n AI-powered tool aimed at streamlining model-based systems engineeringSystems engineering (MBSEModel Based Systems Engineering (MBSE)) workflows. The tool converts natural language inputs into MBSEModel Based Systems Engineering (MBSE) models by combining large language modelsLarge Language Models (LLM) and natural language processing techniques with MBSEModel Based Systems Engineering (MBSE) software APIs. This work also proposes a potential framework for evaluating LLMLarge Language Models (LLM)-generated models, focusing on metrics such as accuracy, completeness, consistency, clarity, and usability. Integrating generative AIGenerative AI into systems engineering processesSystems engineering process is highly effective for automating routine tasks, boosting productivity, and supporting the ongoing digital transformationDigital transformation in the field.