A Multidisciplinary Design and Optimization (MDO) Agent Driven by Large Language Models

TL;DR

This paper introduces a Large Language Model-driven MDO agent that automates and enhances the mechanical design process through natural language understanding, knowledge integration, and automated optimization, reducing manual effort and fostering innovation.

Contribution

It presents a novel LLM-powered MDO agent capable of end-to-end automated design, integrating natural language processing, knowledge retrieval, and software orchestration for engineering tasks.

Findings

Successfully automated design workflows for gas-turbine blades, machine-tool columns, and heat sinks.

Reduced manual scripting and setup effort in the design process.

Enabled innovative design exploration through AI-driven automation.

Abstract

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.