Multi-agent LLM framework integrating CAD, FEA, and optimization

Mechanical design workflow typically involves iterative cycles of computer-aided design (CAD) modeling, finite element analysis, and optimization, each requiring significant expertise and manual effort. Recent advances in large language models (LLMs) have enabled the automation of individual stages, including CAD modeling and finite element analysis. Moreover, several studies have utilized LLMs for mechanical design. However, fully integrated end-to-end automation remains limited. Therefore, this study proposes a novel end-to-end framework based on LLM agents that achieves full automation of the entire design–analysis–optimization workflow. Driven entirely by natural language inputs, the framework integrates automatic parameter extraction, CAD modeling, meshing, structural analysis, and optimization. The proposed framework performs structural analyses and achieves optimization goals while preserving design constraints. This is demonstrated through four case studies: a cantilever beam, two-section bar, desk, and flanged pipe. Notably, the proposed approach preserves critical design parameters that are often implicit, thereby mimicking the decision-making of experienced engineers. These results demonstrate the feasibility of democratizing mechanical design by enabling non-experts to perform sophisticated tasks. Although the current implementation is confined to linear analysis and exhibits reduced robustness in highly complex scenarios, this work provides a promising foundation for AI-driven automation in engineering design.