Xianlin Ren, Chengrui Han, Yiduo Tian, Laixian Chen, B. Liu
{"title":"基于概率直觉模糊集预测模型和三指数加权移动平均控制图的制造质量控制方法研究","authors":"Xianlin Ren, Chengrui Han, Yiduo Tian, Laixian Chen, B. Liu","doi":"10.1109/WCMEIM56910.2022.10021385","DOIUrl":null,"url":null,"abstract":"To address the problem of late warning time of control charts, this paper proposes a method for manufacturing quality control by combining the PIFS-PSNN prediction model with the TEWMA control chart. The method can do pre-alert and is capable of dealing with various uncertainties and ambiguities in the data during the manufacturing process as well as detecting minor shifts in quality characteristics. The fuzzy time series prediction model is employed to predict the manufacturing quality characteristics data and build the control chart based on the predicted data, to detect the shift in the quality characteristics data earlier and feedback to the manufacturing process. Finally, the effectiveness of the proposed method is demonstrated by a case study.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on Manufacturing Quality Control Method Based on the Probabilistic Intuitionistic Fuzzy Set Prediction Model and the Triple Exponentially Weighted Moving Average Control Chart\",\"authors\":\"Xianlin Ren, Chengrui Han, Yiduo Tian, Laixian Chen, B. Liu\",\"doi\":\"10.1109/WCMEIM56910.2022.10021385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To address the problem of late warning time of control charts, this paper proposes a method for manufacturing quality control by combining the PIFS-PSNN prediction model with the TEWMA control chart. The method can do pre-alert and is capable of dealing with various uncertainties and ambiguities in the data during the manufacturing process as well as detecting minor shifts in quality characteristics. The fuzzy time series prediction model is employed to predict the manufacturing quality characteristics data and build the control chart based on the predicted data, to detect the shift in the quality characteristics data earlier and feedback to the manufacturing process. Finally, the effectiveness of the proposed method is demonstrated by a case study.\",\"PeriodicalId\":202270,\"journal\":{\"name\":\"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCMEIM56910.2022.10021385\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCMEIM56910.2022.10021385","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on Manufacturing Quality Control Method Based on the Probabilistic Intuitionistic Fuzzy Set Prediction Model and the Triple Exponentially Weighted Moving Average Control Chart
To address the problem of late warning time of control charts, this paper proposes a method for manufacturing quality control by combining the PIFS-PSNN prediction model with the TEWMA control chart. The method can do pre-alert and is capable of dealing with various uncertainties and ambiguities in the data during the manufacturing process as well as detecting minor shifts in quality characteristics. The fuzzy time series prediction model is employed to predict the manufacturing quality characteristics data and build the control chart based on the predicted data, to detect the shift in the quality characteristics data earlier and feedback to the manufacturing process. Finally, the effectiveness of the proposed method is demonstrated by a case study.