B. V. Souza, S. Santos, André Marcorin de Oliveira, S. Givigi
{"title":"基于梯度增强技术的制造过程虚拟传感器建模","authors":"B. V. Souza, S. Santos, André Marcorin de Oliveira, S. Givigi","doi":"10.1109/SysCon53073.2023.10131061","DOIUrl":null,"url":null,"abstract":"This paper proposes a learning architecture approach for creating a virtual sensor model that detects product failures by using machine operating data from a discrete manufacturing process based on Gradient Boosting and Random Forest algorithms. The main contribution of this work is to propose a methodology for creating the virtual sensor to predict the manufactured product quality with precision equivalent to that obtained by actual sensors. Simulation results showed that the proposed virtual sensor detects precisely manufacturing failures caused by supplement position to produce the target products.","PeriodicalId":169296,"journal":{"name":"2023 IEEE International Systems Conference (SysCon)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling of Virtual Sensors for Manufacturing Process using Gradient Boosting Technique\",\"authors\":\"B. V. Souza, S. Santos, André Marcorin de Oliveira, S. Givigi\",\"doi\":\"10.1109/SysCon53073.2023.10131061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a learning architecture approach for creating a virtual sensor model that detects product failures by using machine operating data from a discrete manufacturing process based on Gradient Boosting and Random Forest algorithms. The main contribution of this work is to propose a methodology for creating the virtual sensor to predict the manufactured product quality with precision equivalent to that obtained by actual sensors. Simulation results showed that the proposed virtual sensor detects precisely manufacturing failures caused by supplement position to produce the target products.\",\"PeriodicalId\":169296,\"journal\":{\"name\":\"2023 IEEE International Systems Conference (SysCon)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE International Systems Conference (SysCon)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SysCon53073.2023.10131061\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Systems Conference (SysCon)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SysCon53073.2023.10131061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling of Virtual Sensors for Manufacturing Process using Gradient Boosting Technique
This paper proposes a learning architecture approach for creating a virtual sensor model that detects product failures by using machine operating data from a discrete manufacturing process based on Gradient Boosting and Random Forest algorithms. The main contribution of this work is to propose a methodology for creating the virtual sensor to predict the manufactured product quality with precision equivalent to that obtained by actual sensors. Simulation results showed that the proposed virtual sensor detects precisely manufacturing failures caused by supplement position to produce the target products.