F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Norimasa Marui
{"title":"基于递归模糊推理的磨削过程表面粗糙度在线估计","authors":"F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Norimasa Marui","doi":"10.1115/imece1997-1074","DOIUrl":null,"url":null,"abstract":"\n Grinding process is frequently used to produce a smooth surface in the manufacturing system. Recently, for using a whetstone for a long time, grinding system needs to measure the surface roughness. However, it is difficult to measure the surface roughness and it takes a long time to measure it in process. Thus, we have to estimate surface roughness in process by online sensing information. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and is applied to a multi-sensor fusion system for estimating the surface roughness. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The learning method of RFI is based on the steepest descent method and incremental learning which can add new fuzzy rules. Where, the shape of new fuzzy rules is determined by the fitness of previous fuzzy rules.","PeriodicalId":432053,"journal":{"name":"Manufacturing Science and Engineering: Volume 1","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online Estimation of Surface Roughness by Recurrent Fuzzy Inference in Grinding Process\",\"authors\":\"F. Kobayashi, F. Arai, T. Fukuda, M. Onoda, Norimasa Marui\",\"doi\":\"10.1115/imece1997-1074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Grinding process is frequently used to produce a smooth surface in the manufacturing system. Recently, for using a whetstone for a long time, grinding system needs to measure the surface roughness. However, it is difficult to measure the surface roughness and it takes a long time to measure it in process. Thus, we have to estimate surface roughness in process by online sensing information. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and is applied to a multi-sensor fusion system for estimating the surface roughness. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The learning method of RFI is based on the steepest descent method and incremental learning which can add new fuzzy rules. Where, the shape of new fuzzy rules is determined by the fitness of previous fuzzy rules.\",\"PeriodicalId\":432053,\"journal\":{\"name\":\"Manufacturing Science and Engineering: Volume 1\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Manufacturing Science and Engineering: Volume 1\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/imece1997-1074\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing Science and Engineering: Volume 1","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/imece1997-1074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Online Estimation of Surface Roughness by Recurrent Fuzzy Inference in Grinding Process
Grinding process is frequently used to produce a smooth surface in the manufacturing system. Recently, for using a whetstone for a long time, grinding system needs to measure the surface roughness. However, it is difficult to measure the surface roughness and it takes a long time to measure it in process. Thus, we have to estimate surface roughness in process by online sensing information. In this paper, we propose a Recurrent Fuzzy Inference (RFI) with recurrent inputs and is applied to a multi-sensor fusion system for estimating the surface roughness. The membership functions of RFI are expressed by Radial Basis Function (RBF) with insensitive ranges. The learning method of RFI is based on the steepest descent method and incremental learning which can add new fuzzy rules. Where, the shape of new fuzzy rules is determined by the fitness of previous fuzzy rules.