Li-Bao An, Peiqing Yang, Hong Zhang, Ming-Ying Chen
{"title":"Multi-Objective Optimization for Milling Operations using Genetic Algorithms under Various Constraints","authors":"Li-Bao An, Peiqing Yang, Hong Zhang, Ming-Ying Chen","doi":"10.2991/ijndc.2014.2.2.5","DOIUrl":null,"url":null,"abstract":"In this paper, the parameter optimization problem for face-milling operations is studied. A multi-objective mathematical model is developed with the purpose to minimize the unit production cost and total machining time while maximize the profit rate. The unwanted material is removed by one finishing pass and at least one roughing passes depending on the total depth of cut. Maximum and minimum allowable cutting speeds, feed rates and depths of cut, as well as tool life, surface roughness, cutting force and cutting power consumption are constraints of the model. Optimal values of objective function and corresponding machining parameters are found by Genetic Algorithms. An example is presented to illustrate the model and solution method.","PeriodicalId":318936,"journal":{"name":"Int. J. Networked Distributed Comput.","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Networked Distributed Comput.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/ijndc.2014.2.2.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
In this paper, the parameter optimization problem for face-milling operations is studied. A multi-objective mathematical model is developed with the purpose to minimize the unit production cost and total machining time while maximize the profit rate. The unwanted material is removed by one finishing pass and at least one roughing passes depending on the total depth of cut. Maximum and minimum allowable cutting speeds, feed rates and depths of cut, as well as tool life, surface roughness, cutting force and cutting power consumption are constraints of the model. Optimal values of objective function and corresponding machining parameters are found by Genetic Algorithms. An example is presented to illustrate the model and solution method.