Enhanced RAG for Knowledge Retrieval in Mechanical Manufacturing

Intelligent question-answering (QA) systems powered by large language models (LLMs) have shown significant promise across various fields, but several challenges still hinder their application in mechanical manufacturing. This domain involves complex textual knowledge and numerous process design parameters, often presented in tabular form, which traditional LLMs struggle to extract effectively. We propose a novel intelligent QA system, MAR, tailored to address this in the mechanical manufacturing domain. MAR integrates open-source LLMs, Retrieval-Augmented Generation (RAG), and knowledge Re-ranking techniques. By combining RAG with Re-ranking, the system significantly enhances the accuracy and efficiency of knowledge retrieval, overcoming the limitations of traditional RAG methods. The system utilizes the LoRA+-fine-tuned Qwen2-7B model alongside a knowledge base containing industry standards and process design data. Experimental results show that the combination of RAG and Re-ranking outperforms traditional LLMs, as evidenced by improvements in BLEU-4 and ROUGE metrics. This research provides a practical solution for intelligent QA in mechanical manufacturing, effectively handling complex knowledge and precision-based tasks.