Intan Maisarah Abd Rahim, F. Mat, S. Yaacob, R. Siregar
{"title":"用非破坏性振动技术对材料力学性能进行分类","authors":"Intan Maisarah Abd Rahim, F. Mat, S. Yaacob, R. Siregar","doi":"10.1109/CSPA.2011.5759873","DOIUrl":null,"url":null,"abstract":"This study is to develop a system of a non-destructive testing on the material to define the mechanical properties of material. The study focused on experimental and testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for training. As an extension for the study, k-Nearest Neighbor classifier is developed to work as a system to classify the materials tested according to their mechanical properties. The result from the classification system shows that k-NN is giving the accuracy of 99.79783 % with the k value of 1.","PeriodicalId":282179,"journal":{"name":"2011 IEEE 7th International Colloquium on Signal Processing and its Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The classification of material mechanical properties using non-destructive vibration technique\",\"authors\":\"Intan Maisarah Abd Rahim, F. Mat, S. Yaacob, R. Siregar\",\"doi\":\"10.1109/CSPA.2011.5759873\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study is to develop a system of a non-destructive testing on the material to define the mechanical properties of material. The study focused on experimental and testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for training. As an extension for the study, k-Nearest Neighbor classifier is developed to work as a system to classify the materials tested according to their mechanical properties. The result from the classification system shows that k-NN is giving the accuracy of 99.79783 % with the k value of 1.\",\"PeriodicalId\":282179,\"journal\":{\"name\":\"2011 IEEE 7th International Colloquium on Signal Processing and its Applications\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 7th International Colloquium on Signal Processing and its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSPA.2011.5759873\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 7th International Colloquium on Signal Processing and its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSPA.2011.5759873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The classification of material mechanical properties using non-destructive vibration technique
This study is to develop a system of a non-destructive testing on the material to define the mechanical properties of material. The study focused on experimental and testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for training. As an extension for the study, k-Nearest Neighbor classifier is developed to work as a system to classify the materials tested according to their mechanical properties. The result from the classification system shows that k-NN is giving the accuracy of 99.79783 % with the k value of 1.