{"title":"基于ANFIS的翼面磨损建模","authors":"Khaled Zid, M. Ben Ahmed, Mourad Turki","doi":"10.1145/3234698.3234745","DOIUrl":null,"url":null,"abstract":"This paper presents the modeling of Flank Wear of machining hot work tool steel (AISI H11) with ceramic tools. This work determined the effect of various cutting parameters:cutting speed, feed rate, depth of cut and nose radius on Flank Wear. Two models have been proposed, the first model is Taguchi method and the second one is adaptive neuro-fuzzy inference system ANFIS. The results obtained by the two models are compared with the experimental results.","PeriodicalId":144334,"journal":{"name":"Proceedings of the Fourth International Conference on Engineering & MIS 2018","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Modeling of Flank Wear Using ANFIS\",\"authors\":\"Khaled Zid, M. Ben Ahmed, Mourad Turki\",\"doi\":\"10.1145/3234698.3234745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the modeling of Flank Wear of machining hot work tool steel (AISI H11) with ceramic tools. This work determined the effect of various cutting parameters:cutting speed, feed rate, depth of cut and nose radius on Flank Wear. Two models have been proposed, the first model is Taguchi method and the second one is adaptive neuro-fuzzy inference system ANFIS. The results obtained by the two models are compared with the experimental results.\",\"PeriodicalId\":144334,\"journal\":{\"name\":\"Proceedings of the Fourth International Conference on Engineering & MIS 2018\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fourth International Conference on Engineering & MIS 2018\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3234698.3234745\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fourth International Conference on Engineering & MIS 2018","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3234698.3234745","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents the modeling of Flank Wear of machining hot work tool steel (AISI H11) with ceramic tools. This work determined the effect of various cutting parameters:cutting speed, feed rate, depth of cut and nose radius on Flank Wear. Two models have been proposed, the first model is Taguchi method and the second one is adaptive neuro-fuzzy inference system ANFIS. The results obtained by the two models are compared with the experimental results.
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