LLM-Driven Framework for Industrial Design Automation

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.