{"title":"Research on Intelligent Process Quality Control System under Network Environment","authors":"Jun Guo, Shunsheng Guo, Xiaobing Yu","doi":"10.1109/ISME.2010.29","DOIUrl":null,"url":null,"abstract":"In the networked manufacturing environment, how to monitor dynamic and variable quality fluctuation, diagnose the abnormal variation in real-time and adjust the process at the right moment, becomes a difficult problem for the modern networked manufacturing enterprise in process quality control. In order to overcome this difficulty, This paper presents an intelligent process quality control mode oriented to networked manufacturing by combining statistical process control (SPC), and artificial intelligent (AI) for quality prevention, online statistical analysis, intelligent diagnosis and adjustment, and corresponding functional modules and framework is put forward. Finally, the prototype system of intelligent process quality control is developed to demonstrate the rationality and validity of the method. This work confirms the potential synergies of hybrid AI techniques for realizing networking, intelligent and automatic process quality control.","PeriodicalId":348878,"journal":{"name":"2010 International Conference of Information Science and Management Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference of Information Science and Management Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISME.2010.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the networked manufacturing environment, how to monitor dynamic and variable quality fluctuation, diagnose the abnormal variation in real-time and adjust the process at the right moment, becomes a difficult problem for the modern networked manufacturing enterprise in process quality control. In order to overcome this difficulty, This paper presents an intelligent process quality control mode oriented to networked manufacturing by combining statistical process control (SPC), and artificial intelligent (AI) for quality prevention, online statistical analysis, intelligent diagnosis and adjustment, and corresponding functional modules and framework is put forward. Finally, the prototype system of intelligent process quality control is developed to demonstrate the rationality and validity of the method. This work confirms the potential synergies of hybrid AI techniques for realizing networking, intelligent and automatic process quality control.