Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework

TL;DR

This paper presents a multi-agent AI framework that integrates knowledge graphs and iterative review loops to automate and improve complex engineering design processes, demonstrated through aerodynamic optimization of airfoils.

Contribution

It introduces a novel multi-agent system with knowledge-driven agents that collaboratively generate and refine engineering designs using structured knowledge graphs.

Findings

Effective design generation with knowledge graphs

Iterative review improves design quality

Framework enhances efficiency and consistency

Abstract

The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops. The framework introduces specialized knowledge-driven agents that collaborate to generate and refine design candidates. As an exemplar, we demonstrate its application to the aerodynamic optimization of 4-digit NACA airfoils. The framework consists of three key AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer. The Graph Ontologist employs a Large Language Model (LLM) to construct two domain-specific knowledge graphs from airfoil design literature. The Systems Engineer, informed by a human manager, formulates technical requirements that guide design generation and evaluation. The Design Engineer leverages the design knowledge graph and computational tools to propose candidate airfoils meeting these requirements. The Systems Engineer reviews and provides feedback both qualitative and quantitative using its own knowledge graph, forming an iterative feedback loop until a design is validated by the manager. The final design is then optimized to maximize performance metrics such as the lift-to-drag ratio. Overall, this work demonstrates how collaborative AI agents equipped with structured knowledge representations can enhance efficiency, consistency, and quality in the engineering design process.