Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge in engineering design. Traditional methods treat modularization as a pure graph optimization, without access to the engineering context embedded in the system. Building on prior work on LLM-based combinatorial optimization for DSM sequencing, this paper extends the method to modularization across five cases and three backbone LLMs. Our method achieves near-reference quality within 30 iterations without requiring specialized optimization code. Counterintuitively, domain knowledge, beneficial in sequencing, consistently impairs performance on more complex DSMs. We attribute this to semantic misalignment between the LLM's functional priors and the purely structural optimization objective, and propose the semantic-alignment hypothesis as a testable condition governing knowledge effectiveness with LLMs. Ablation studies identify the most effective input representation, objective formulation, and solution pool design for practical deployment. These findings offer practical guidance for deploying LLMs in engineering design optimization.

翻译：设计结构矩阵（DSM）模块化是将系统元素划分为内聚模块的任务，是工程设计中的基础组合挑战。传统方法将模块化视为纯图优化问题，无法获取嵌入系统中的工程上下文信息。基于先前利用大语言模型进行DSM排序组合优化的研究，本文将该方法扩展至五个案例及三种骨干大语言模型的模块化任务中。我们的方法在30次迭代内即可达到接近参考解的质量，且无需专用优化代码。与直觉相悖的是，在排序任务中表现良好的领域知识，在处理更复杂的DSM时却持续损害性能。我们将此归因于大语言模型的功能先验与纯结构优化目标之间的语义错位，并提出“语义对齐假说”作为检验知识有效性的可测试条件。消融研究确定了实际部署中最有效的输入表示、目标函数构建及解池设计方案。这些发现为在工程设计优化中部署大语言模型提供了实践指导。