{"title":"Fuzzy inference system based intelligent sensor fusion for estimation of surface roughness in machining process","authors":"R. K. Barai, T. Tjahjowidodo, Bobby K. Pappachan","doi":"10.1109/ICSENST.2015.7438506","DOIUrl":null,"url":null,"abstract":"Measurement of surface roughness of any machining process is crucial for obtaining a component or part of the correct size and surface finish in the first instance, in order to minimize the manufacturing cost. In-process monitoring of machining processes based on an estimation of the surface roughness using the cutting parameters is inaccurate. In this investigation, a fuzzy inference system based on an intelligent sensor fusion model has been developed for the purpose of in-process indirect measurement of surface roughness for a machining process. In the proposed technique, measurement of the Speed Force component, Radial Force component, Feed Force component, Vibration, and Acoustic Emission sensor inputs from a turning process have been considered as the inputs. The results have been compared with the surface roughness estimated with a second order regression model using cutting parameters as inputs. The proposed method has shown considerable improvement in the surface roughness estimation in a simulation environment.","PeriodicalId":375376,"journal":{"name":"2015 9th International Conference on Sensing Technology (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 9th International Conference on Sensing Technology (ICST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENST.2015.7438506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Measurement of surface roughness of any machining process is crucial for obtaining a component or part of the correct size and surface finish in the first instance, in order to minimize the manufacturing cost. In-process monitoring of machining processes based on an estimation of the surface roughness using the cutting parameters is inaccurate. In this investigation, a fuzzy inference system based on an intelligent sensor fusion model has been developed for the purpose of in-process indirect measurement of surface roughness for a machining process. In the proposed technique, measurement of the Speed Force component, Radial Force component, Feed Force component, Vibration, and Acoustic Emission sensor inputs from a turning process have been considered as the inputs. The results have been compared with the surface roughness estimated with a second order regression model using cutting parameters as inputs. The proposed method has shown considerable improvement in the surface roughness estimation in a simulation environment.