摘要
为帮助企业识别产品问题和设计缺陷,提高产品满意度,需要对离散制造业产品质量进行大数据分析。阐述制造业产品质量大数据的特点,从数据采集处理、质量预测、质量控制和质量追溯4个环节,综述离散制造业产品质量分析的国内外研究进展和发展动态,并指出各类理论方法在大数据背景下的挑战,探讨应对这些挑战的解决途径与发展趋势。结果表明,该分析可为进一步展开研究提供参考。
In order to help enterprises identify product problems and design defects and improve product satisfaction,it is necessary to analyze the product quality of discrete manufacturing industry with big data.This paper describes the characteristics of product quality big data in manufacturing industry,summarizes the research progress and development trends of product quality analysis in discrete manufacturing industry at home and abroad from four aspects of data acquisition and processing,quality prediction,quality control and quality traceability,points out the challenges of various theoretical methods under the background of big data,and discusses the solutions and development trends to deal with these challenges.The results show that the analysis can provide a reference for further research.
作者
李君妍
胡欣
刘治红
石义官
张瀚铭
Li Junyan;Hu Xin;Liu Zhihong;Shi Yiguan;Zhang Hanming(Changsha Branch,Automation Research Institute Co.,Ltd.of China South Industries Group Co.,Ltd.,Mianyang 621000,China;Military Representative Office in Guangyuan District,Army Equipment Department,Guangyuan 628000,China;School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《兵工自动化》
2023年第11期23-27,共5页
Ordnance Industry Automation
基金
国防基础科研项目(JCKY2020209B002)。
关键词
智能制造
质量分析
质量预测
质量控制
质量追溯
intelligent manufacturing
quality analysis
quality prediction
quality control
quality traceability
