{"title":"MOSA算法在Gleeble测试模型更新中的应用","authors":"Dong Xu, K. Zhou, J. Tang","doi":"10.1115/isfa2020-9646","DOIUrl":null,"url":null,"abstract":"\n This research concerns the parametric identification of Johnson-Cook constitutive model which is frequently used to describe the mechanical behavior of metal material at high temperature. An improved multi-objective simulated annealing (MOSA) algorithm is introduced to update Johnson-Cook model based on Gleeble testing data for Steel T24. Our case study produces Pareto solutions ranked by the error corresponding to each parameter to be optimized. This algorithm improves the previous methods and yields a more suitable solution corresponding to the actual situation.","PeriodicalId":159740,"journal":{"name":"2020 International Symposium on Flexible Automation","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of MOSA Algorithm in Gleeble Testing Model Updating\",\"authors\":\"Dong Xu, K. Zhou, J. Tang\",\"doi\":\"10.1115/isfa2020-9646\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This research concerns the parametric identification of Johnson-Cook constitutive model which is frequently used to describe the mechanical behavior of metal material at high temperature. An improved multi-objective simulated annealing (MOSA) algorithm is introduced to update Johnson-Cook model based on Gleeble testing data for Steel T24. Our case study produces Pareto solutions ranked by the error corresponding to each parameter to be optimized. This algorithm improves the previous methods and yields a more suitable solution corresponding to the actual situation.\",\"PeriodicalId\":159740,\"journal\":{\"name\":\"2020 International Symposium on Flexible Automation\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Flexible Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/isfa2020-9646\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Flexible Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/isfa2020-9646","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of MOSA Algorithm in Gleeble Testing Model Updating
This research concerns the parametric identification of Johnson-Cook constitutive model which is frequently used to describe the mechanical behavior of metal material at high temperature. An improved multi-objective simulated annealing (MOSA) algorithm is introduced to update Johnson-Cook model based on Gleeble testing data for Steel T24. Our case study produces Pareto solutions ranked by the error corresponding to each parameter to be optimized. This algorithm improves the previous methods and yields a more suitable solution corresponding to the actual situation.