{"title":"机械加工过程中表面粗糙度测量建模的人工神经网络技术综述","authors":"A. Zain, H. Haron, S. Sharif","doi":"10.1109/AMS.2009.78","DOIUrl":null,"url":null,"abstract":"The former, which is defined as modeling of machining processes, is essential to provide the basic mathematical models for formulation of the certain process objective functions. With conventional approaches such as Statistical Regression technique, explicit models are developed that required complex physical understanding of the modeling process. With non conventional approaches or Artificial Intelligence techniques such as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm based modeling, implicit model are created within the weight matrices of the net, rules and genes that is easier to be implemented. With the focus on surface roughness performance measure, this paper outlines and discusses the concept, application, abilities and limitations of Artificial Neural Network in the machining process modeling. Subsequently the future trend of Artificial Neural Network in modeling machining process is reported.","PeriodicalId":6461,"journal":{"name":"2009 Third Asia International Conference on Modelling & Simulation","volume":"44 1","pages":"35-39"},"PeriodicalIF":0.0000,"publicationDate":"2009-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Review of ANN Technique for Modeling Surface Roughness Performance Measure in Machining Process\",\"authors\":\"A. Zain, H. Haron, S. Sharif\",\"doi\":\"10.1109/AMS.2009.78\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The former, which is defined as modeling of machining processes, is essential to provide the basic mathematical models for formulation of the certain process objective functions. With conventional approaches such as Statistical Regression technique, explicit models are developed that required complex physical understanding of the modeling process. With non conventional approaches or Artificial Intelligence techniques such as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm based modeling, implicit model are created within the weight matrices of the net, rules and genes that is easier to be implemented. With the focus on surface roughness performance measure, this paper outlines and discusses the concept, application, abilities and limitations of Artificial Neural Network in the machining process modeling. Subsequently the future trend of Artificial Neural Network in modeling machining process is reported.\",\"PeriodicalId\":6461,\"journal\":{\"name\":\"2009 Third Asia International Conference on Modelling & Simulation\",\"volume\":\"44 1\",\"pages\":\"35-39\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Third Asia International Conference on Modelling & Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMS.2009.78\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Third Asia International Conference on Modelling & Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2009.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Review of ANN Technique for Modeling Surface Roughness Performance Measure in Machining Process
The former, which is defined as modeling of machining processes, is essential to provide the basic mathematical models for formulation of the certain process objective functions. With conventional approaches such as Statistical Regression technique, explicit models are developed that required complex physical understanding of the modeling process. With non conventional approaches or Artificial Intelligence techniques such as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm based modeling, implicit model are created within the weight matrices of the net, rules and genes that is easier to be implemented. With the focus on surface roughness performance measure, this paper outlines and discusses the concept, application, abilities and limitations of Artificial Neural Network in the machining process modeling. Subsequently the future trend of Artificial Neural Network in modeling machining process is reported.