基于大语言模型的结构件加工工艺推荐方法研究

doi:10.3901/JME.2025.17.393

机械工程学报 ›› 2025, Vol. 61 ›› Issue (17): 393-404.doi: 10.3901/JME.2025.17.393

• 制造工艺与装备 • 上一篇

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基于大语言模型的结构件加工工艺推荐方法研究

郑小虎^1,2, 陈宏博³, 何方舟⁴

1. 东华大学人工智能研究院上海 201620;
2. 上海工业大数据与智能系统工程技术研究中心上海 201620;
3. 东华大学机械工程学院上海 201620;
4. 上海飞机制造有限公司上海 201324

收稿日期:2024-03-31 修回日期:2024-12-09 发布日期:2025-10-24
作者简介:郑小虎(通信作者)，男，1983年出生，博士，副教授，硕士研究生导师。主要研究方向为设备智能运维技术、知识图谱、深度学习、智能工艺与仿真。E-mail：xhzheng@dhu.edu.cn;陈宏博，男，2000年出生，硕士研究生。主要研究方向为数字孪生和大语言模型。E-mail：hbchen@mail.dhu.edu.cn
基金资助:
国家自然科学基金青年科学基金(51805079)和中央高校基本科研业务费专项资金(2232021D-15)资助项目。

Research on Machining Process Recommendation Method of Structural Parts Based on Large Language Models

ZHENG Xiaohu^1,2, CHEN Hongbo³, HE Fangzhou⁴

1. Institute of Artificial Intelligence, Donghua University, Shanghai 201620;
2. Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center, Shanghai 201620;
3. College of Mechancial Engineering, Donghua University, Shanghai 201620;
4. Shanghai Aircraft Manufacturing Company Limited, Shanghai 201324

Received:2024-03-31 Revised:2024-12-09 Published:2025-10-24

摘要/Abstract

摘要： 在复杂结构件数控编程过程中，由于机械加工工艺知识的多源异构及知识之间的关联复杂性，导致加工工艺知识重用困难。提出一种基于大语言模型的结构件加工工艺知识推荐方法，通过大语言模型的选择和微调，建立结构件加工工艺知识推荐的垂直领域模型，效果评估表明该模型可以针对具体零件特征推荐出相应的加工工艺。为解决模型无法获取最新专业知识且加工工艺推荐准确度低的问题，利用LangChain应用框架结合知识库对领域模型进行知识检索增强并构建工艺知识问答系统，通过相应指标评价，实现了该问答系统在原有领域模型的基础上F1值提升0.026，加工工艺推荐准确度在90%以上。在航空结构件数控编程的工艺决策应用中，该方法针对零件特征实现了相应工艺知识的推荐，与未使用本文方法的自动数控编程系统相比，框体类结构件数控代码生成效率具有一定提升，对提高数控编程人员的决策效率具有重要意义。

关键词: 大语言模型, 加工工艺, 知识推荐, 快速编程

Abstract: In the process of numerical control programming for complex structural components, the difficulty in reusing machining process knowledge arises due to the heterogeneity of knowledge sources and the complexity of interconnections between knowledge. A knowledge recommendation method for structural parts machining process based on a large language model is proposed. By selecting and fine-tuning the large language model, a vertical domain model of machining process knowledge recommendation for structural parts is established. The evaluation results indicate that the model can recommend corresponding machining processes based on specific part features. To solve the problem of the model not being able to obtain the latest professional knowledge and the low accuracy of machining process recommendations, the LangChain application framework combined with a knowledge base is used to enhance the knowledge retrieval of the domain model and construct a process knowledge question answering system. Through corresponding indicator evaluation, the F1 value of the question answering system improves by 0.026 on the basis of the original domain model, and the accuracy of machining process recommendations is above 90%. In the process decision-making application of CNC programming for aviation structural components, this method recommends corresponding process knowledge based on part features. Compared with the automatic CNC programming system that does not use the method in this article, the efficiency of generating CNC codes for frame type structural components improves to a certain extent, which is of great significance for improving the decision-making efficiency of CNC programmers.

Key words: large language models, machining process, knowledge recommendation, rapid NC programming

中图分类号:

TH16

郑小虎, 陈宏博, 何方舟. 基于大语言模型的结构件加工工艺推荐方法研究[J]. 机械工程学报, 2025, 61(17): 393-404.

ZHENG Xiaohu, CHEN Hongbo, HE Fangzhou. Research on Machining Process Recommendation Method of Structural Parts Based on Large Language Models[J]. Journal of Mechanical Engineering, 2025, 61(17): 393-404.

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基于大语言模型的结构件加工工艺推荐方法研究

Research on Machining Process Recommendation Method of Structural Parts Based on Large Language Models

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