{"title":"利用响应面、神经网络和遗传算法对焊接工艺参数进行优化","authors":"R.J. Praga-Alejo, L.M. Torres-Trevio, M.R. Pia-Monarrez","doi":"10.1109/CERMA.2008.70","DOIUrl":null,"url":null,"abstract":"Since the Neural Network (NN) with a Genetic Algorithm (GA) as a complement; are good optimization tools, we compare its performance with the Response Surface Methodology (RSM) that is generally used in the optimization of the process, in this case welding process. For the data used in the comparison, the results show that NN plus GA and RSM have a good results and very well performance, for identify the optimal set of parameters to obtain amaximum response of the process.","PeriodicalId":126172,"journal":{"name":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Optimization Welding Process Parameters through Response Surface, Neural Network and Genetic Algorithms\",\"authors\":\"R.J. Praga-Alejo, L.M. Torres-Trevio, M.R. Pia-Monarrez\",\"doi\":\"10.1109/CERMA.2008.70\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the Neural Network (NN) with a Genetic Algorithm (GA) as a complement; are good optimization tools, we compare its performance with the Response Surface Methodology (RSM) that is generally used in the optimization of the process, in this case welding process. For the data used in the comparison, the results show that NN plus GA and RSM have a good results and very well performance, for identify the optimal set of parameters to obtain amaximum response of the process.\",\"PeriodicalId\":126172,\"journal\":{\"name\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CERMA.2008.70\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 Electronics, Robotics and Automotive Mechanics Conference (CERMA '08)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CERMA.2008.70","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimization Welding Process Parameters through Response Surface, Neural Network and Genetic Algorithms
Since the Neural Network (NN) with a Genetic Algorithm (GA) as a complement; are good optimization tools, we compare its performance with the Response Surface Methodology (RSM) that is generally used in the optimization of the process, in this case welding process. For the data used in the comparison, the results show that NN plus GA and RSM have a good results and very well performance, for identify the optimal set of parameters to obtain amaximum response of the process.