A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866917098024861696 |
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| author | Guo, Bingkun Li, Wentian Liu, Xiaojian Luo, Jiaqi Yu, Zibin Dong, Dalong Zhang, Shuyou Zhang, Yiming |
| author_facet | Guo, Bingkun Li, Wentian Liu, Xiaojian Luo, Jiaqi Yu, Zibin Dong, Dalong Zhang, Shuyou Zhang, Yiming |
| contents | To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization. Working in tandem, these capabilities interpret high-level, unstructured intent, translate it into structured design representations, automatically construct parametric 3D CAD models, generate reliable concept variants using external knowledge bases, and conduct evaluation with iterative optimization via tool calls such as finite-element analysis (FEA). Validation on three representative cases - a gas-turbine blade, a machine-tool column, and a fractal heat sink - shows that the agent completes the pipeline from natural-language intent to verified and optimized designs with reduced manual scripting and setup effort, while promoting innovative design exploration. This work points to a practical path toward human-AI collaborative mechanical engineering and lays a foundation for more dependable, vertically customized MDO systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models Guo, Bingkun Li, Wentian Liu, Xiaojian Luo, Jiaqi Yu, Zibin Dong, Dalong Zhang, Shuyou Zhang, Yiming Human-Computer Interaction Artificial Intelligence To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (RAG) for knowledge-grounded conceptualization, and (iii) intelligent orchestration of engineering software for performance verification and optimization. Working in tandem, these capabilities interpret high-level, unstructured intent, translate it into structured design representations, automatically construct parametric 3D CAD models, generate reliable concept variants using external knowledge bases, and conduct evaluation with iterative optimization via tool calls such as finite-element analysis (FEA). Validation on three representative cases - a gas-turbine blade, a machine-tool column, and a fractal heat sink - shows that the agent completes the pipeline from natural-language intent to verified and optimized designs with reduced manual scripting and setup effort, while promoting innovative design exploration. This work points to a practical path toward human-AI collaborative mechanical engineering and lays a foundation for more dependable, vertically customized MDO systems. |
| title | A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2511.17511 |