{"title":"Optimization of machining parameters using estimation of distribution algorithms","authors":"Shu-tong Xie, Yinbiao Guo, H. Huang, Jing Lin","doi":"10.1109/ICICISYS.2009.5357778","DOIUrl":null,"url":null,"abstract":"In computer numerical control (CNC) machining problems, it is important to reduce the production cost. To deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in multi-pass turning operations, two estimation of distribution algorithms (EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining parameters. Computer simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters, which significantly reduce the unit production cost.","PeriodicalId":206575,"journal":{"name":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","volume":"66 14","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Intelligent Computing and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICISYS.2009.5357778","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In computer numerical control (CNC) machining problems, it is important to reduce the production cost. To deal with the nonlinear optimization problem of machining parameters which aims to minimize the unit production cost (UC) in multi-pass turning operations, two estimation of distribution algorithms (EDAs) incorporated with gene repair method are proposed to search the optimal solution for machining parameters. Computer simulation results show that the proposed algorithms are efficient in searching the optimal machining parameters, which significantly reduce the unit production cost.