LLM-Driven Framework for Industrial Design Automation

Sicheng He; Xiaoxu Wang; Zeke Chen; Jianxing Liao; Bo Wang; Junyan Xu; Xiaohong Guan; Shui Yu; Yun Li

doi:10.1016/j.eng.2026.04.009

Engineering ›› :202604009 DOI: 10.1016/j.eng.2026.04.009

research-article

LLM-Driven Framework for Industrial Design Automation

Sicheng He ^a^,^b
, Xiaoxu Wang ^a^,^b
, Zeke Chen ^a^,^b
, Jianxing Liao ^a^,^b
, Bo Wang ^a^,^b
, Junyan Xu ^a^,^b
, Xiaohong Guan ^c
, Shui Yu ^a^,^b^,^*
, Yun Li ^a^,^b^,^c^,^*

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Abstract

The rapid advances of large language models (LLMs) have presented industrial sectors with transformative opportunities for innovative designs beyond the capabilities of human designers. Due to the inherent black-box nature of LLMs, however, existing LLM-based design frameworks lack the explainability and robustness that human designers would otherwise offer. To address this issue, we propose an LLM-driven Industrial Design Automation (LLM-IDA) framework to encompass conceptual design, knowledge-based design, and digital prototyping. The LLM-IDA utilizes a multi-tiered artificial intelligence (AI) agent group for four modular processes: ① a specification module, ② a quantification module, ③ a surrogate module, and ④ a prototype module. With every module autonomously executed by the agent group, LLM-IDA enhances the generation of novel designs with few-shot prompts and completes the design process with minimal human intervention. To validate this method, the LLM-IDA is tested and compared with a state-of-the-art retrieval augmented generation (RAG) method using the pass@10 metric. Ablation tests confirm the positive impact of each module on both time costs and optimization performance. Overall, the experimental results show that the LLM-IDA delivers performance superior to the RAG method in real-world applications.

Keywords

Computer-aided design / Large language models / Industrial design automation / Multi-agent systems / Design optimization

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Sicheng He, Xiaoxu Wang, Zeke Chen, Jianxing Liao, Bo Wang, Junyan Xu, Xiaohong Guan, Shui Yu, Yun Li. LLM-Driven Framework for Industrial Design Automation. Engineering 202604009 DOI:10.1016/j.eng.2026.04.009

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