基于数字孪生和知识管理的微型叶片制造过程关键质量管控研究  

Study on Critical Quality Control of Micro-vane Manufacturing Process Based on Digital Twin and Knowledge Management

作　　者：吴中义 吕侃 马鑫楠 王鹏 吕璇 WU Zhongyi;LV Kan;MA Xinnan;WANG Peng;LV Xuan(Chenlong Group Limited Liability Company,Lishui Zhejiang 323000,China;Physics Research Station,School of Physics and Information Technology,Shaanxi Normal University,Xi'an Shaanxi 710062,China)

机构地区：[1]晨龙集团有限责任公司,浙江丽水323000 [2]陕西师范大学物理学与信息技术学院物理学科研流动站,陕西西安710062

出　　处：《机床与液压》2025年第3期115-122,共8页Machine Tool & Hydraulics

基　　金：国家自然科学基金项目(72361024);江西省教育厅科学技术研究项目(GJJ2201520);校企合作横向课题。

摘　　要：微型叶片作为机床和液压系统中不可或缺的精密组件,其高性能标准的实现依赖于执行严苛的质量控制流程。提出一种基于数字孪生和知识管理的创新方法,旨在优化微型叶片制造过程的质量管控流程。构建一个复杂的多维数字孪生模型,该模型结合了物理叶片的属性和虚拟表征,通过知识管理的方法来精准监控和管理生产中的关键质量参数。进一步地,通过实际制造案例分析了现有质量管控流程的局限,并基于智能制造生产线的发展,提出一种动态知识库支持的过程质量控制方案。该研究不仅对微型叶片的制造具有实际意义,同时也为其他高精度加工的质量控制提供参考。The realization of high performance standards for micro-vane,an indispensable precision component in machine tools and hydraulic systems,relies on the implementation of stringent quality control processes.An innovative approach based on digital twin and knowledge management technologies was proposed aiming at optimizing the quality control processes for the manufacturing of micro vanes.A complex,multi-dimensional digital twin model was developed,integrating the physical attributes of the vanes with their virtual representations.In this model,knowledge management technique was employed for the precise monitoring and management of key quality parameters during production.Furthermore,the limitations of existing quality control processes was analyzed through real manufacturing case studies,and in line with the advancements in smart manufacturing lines,a dynamic knowledge base supported process quality control solution was proposed.The research not only has practical implications for the micro-vane manufacturing,but also provides reference for quality control in other high-precision manufacturing.

关 键 词：微型叶片制造 质量管控 数字孪生 知识管理 动态知识库 

分 类 号：TH137.5[机械工程—机械制造及自动化]