制造过程关键质量特性辨识的研究现状及展望  

Research review on identification of key quality characteristics in manufacturing process

作　　者：吴中义 周为 吕侃 晏建武[1] 陈哲[1] WU Zhongyi;ZHOU Wei;LV Kan;YAN Jianwu;CHEN Zhe(School of Mechanical Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Chenlong Group Co.,LTD.,Lishui 321400,China)

机构地区：[1]南昌工程学院机械工程学院,江西南昌330099 [2]晨龙集团有限责任公司,浙江丽水321400

出　　处：《南昌工程学院学报》2024年第3期65-74,共10页Journal of Nanchang Institute of Technology

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

摘　　要：正确识别并控制制造过程的关键质量特性(Key Quality Characteristics,KQCs)对确保产品满足严格的功能、性能和质量标准至关重要。随着自动化和信息技术的进步,基于数据的分析技术成为识别KQCs的核心。本文回顾了KQCs识别方法从直觉到数据科学技术的演变进程,探讨了质量工程、数学模型、机器学习和深度学习等方法在此领域的应用,发现传统方法与先进技术的结合能更有效地实现KQCs参数辨识;展望了在智能制造中利用先进技术和适应性控制策略优化制造流程的未来方向,可为质量控制的持续创新提供理论和实践指导,推动新质生产力发展。Correctly identifying and controlling the key quality characteristics(KQCs)of the manufacturing process is critical to ensure that the products meet the stringent function,performance and quality standards.With advancements in automation and information technology,data-based analysis has become the core of KQCs identification.This paper reviews the evolution of KQC identification methods from intuitive to data science techniques,and discusses the application of quality engineering,statistical models,machine learning,and deep learning in this area.The research suggests that combining traditional methods with advanced technologies can more effectively control KQCs.The article also suggests the future directions for optimizing manufacturing processes in intelligent manufacturing,leveraging advanced technologies and adaptive control strategies,which may provide a theoretical and practical guide for continuous innovation in quality control and promote the development of new productive forces.

关 键 词：制造过程 关键质量特性 参数辨识 机器学习 深度学习 

分 类 号：TH166[机械工程—机械制造及自动化]