A Multiagent Architecture of Requirement-Driven Mechanical System Optimization

Addressing the high cost of building optimization models in conceptual mechanical design, this article presents MecSOAgent, a closed-loop multiagent framework that leverages large language models (LLMs) and an auditable mechanical system optimization graph (MSOG) to translate natural-language requirements into executable optimization scripts and SysML 2.0 physical system models. Domain knowledge is injected via retrieval-augmented generation, while the MSOG persists a traceable mapping from requirements to physical entities, design variables, constraints, objectives, and solution algorithms, enabling consistency checking and engineering audit. To improve reliability, MecSOAgent introduces a deterministic intermediate representation based on a design structure matrix (DSM) that converts relation matrices into incremental updates of the Knowledge Graph, and decouples multiobjective preference modeling from solving via analytic hierarchy process (AHP) with consistency checks. A checklist-based reviewer reconciles graph artifacts with solver outputs to trigger iterative repair. A case study on redundancy configuration optimization for an airborne hydraulic actuation system, along with multidimensional comparative experiments against end-to-end LLMs and general-purpose programming agents, demonstrates that MecSOAgent effectively mitigates semantic drift and physical-logic deficiencies. By automatically instantiating system-level optimization models and generating verifiable artifacts, the proposed framework approaches human-expert reliability, offering a practical path toward Model-Based Systems Engineering-oriented automation of mechanical system optimization design.