{"title":"小数据集评价主曲线材料不均匀性","authors":"K. Wallin","doi":"10.1115/PVP2018-84297","DOIUrl":null,"url":null,"abstract":"The standard Master Curve (MC) deals only with materials assumed to be homogeneous, but MC analysis methods for inhomogeneous materials have also been developed. Especially the bi-modal and multi-modal analysis methods are becoming more and more standard. Their drawback is that these methods are generally reliable only with sufficiently large data sets (number of valid tests, r ≥ 15–20). Here, the possibility of using the multi-modal analysis method with smaller data sets is assessed, and a new procedure to conservatively account for possible inhomogeneities is proposed.","PeriodicalId":128383,"journal":{"name":"Volume 1A: Codes and Standards","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessment of Master Curve Material Inhomogeneity Using Small Data Sets\",\"authors\":\"K. Wallin\",\"doi\":\"10.1115/PVP2018-84297\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The standard Master Curve (MC) deals only with materials assumed to be homogeneous, but MC analysis methods for inhomogeneous materials have also been developed. Especially the bi-modal and multi-modal analysis methods are becoming more and more standard. Their drawback is that these methods are generally reliable only with sufficiently large data sets (number of valid tests, r ≥ 15–20). Here, the possibility of using the multi-modal analysis method with smaller data sets is assessed, and a new procedure to conservatively account for possible inhomogeneities is proposed.\",\"PeriodicalId\":128383,\"journal\":{\"name\":\"Volume 1A: Codes and Standards\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 1A: Codes and Standards\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/PVP2018-84297\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 1A: Codes and Standards","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/PVP2018-84297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessment of Master Curve Material Inhomogeneity Using Small Data Sets
The standard Master Curve (MC) deals only with materials assumed to be homogeneous, but MC analysis methods for inhomogeneous materials have also been developed. Especially the bi-modal and multi-modal analysis methods are becoming more and more standard. Their drawback is that these methods are generally reliable only with sufficiently large data sets (number of valid tests, r ≥ 15–20). Here, the possibility of using the multi-modal analysis method with smaller data sets is assessed, and a new procedure to conservatively account for possible inhomogeneities is proposed.