10% SiC车削A356刀具磨损的回归分析

U. Prakash, Yogavardhanaswamy G.N, S. L. Ajit prasad, H. Ravindra, T. Rajan
{"title":"10% SiC车削A356刀具磨损的回归分析","authors":"U. Prakash, Yogavardhanaswamy G.N, S. L. Ajit prasad, H. Ravindra, T. Rajan","doi":"10.1109/RAICS.2011.6069397","DOIUrl":null,"url":null,"abstract":"In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased predominantly. The need for accurate machining of these composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In general 70% of the components need machining to attain the final shape. In the present work, the tool wear has been studied in this paper by turning the composite bars using HSS and Carbide tools. The paper presents the results of experimental investigation machinability properties of silicon carbide particle (SiC-p) reinforced aluminum metal matrix composite. The effect of machining parameters, e.g. cutting speed, feed rate and depth of cut on tool wear and surface roughness was studied. Machinability properties of the selected material were studied using HSS and Carbide tool material; surface roughness was generally affected by feed rate and cutting speed. Hence the tool wear were measured at different speed and feed conditions. Experimental data collected are tested with Multiple Regression Analysis. On completion of the experimental test, multiple regression analysis is used to predict the wear behavior of the system under any condition within the operating range.","PeriodicalId":394515,"journal":{"name":"2011 IEEE Recent Advances in Intelligent Computational Systems","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Tool wear prediction by Regression Analysis in turning A356 with 10% SiC\",\"authors\":\"U. Prakash, Yogavardhanaswamy G.N, S. L. Ajit prasad, H. Ravindra, T. Rajan\",\"doi\":\"10.1109/RAICS.2011.6069397\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased predominantly. The need for accurate machining of these composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In general 70% of the components need machining to attain the final shape. In the present work, the tool wear has been studied in this paper by turning the composite bars using HSS and Carbide tools. The paper presents the results of experimental investigation machinability properties of silicon carbide particle (SiC-p) reinforced aluminum metal matrix composite. The effect of machining parameters, e.g. cutting speed, feed rate and depth of cut on tool wear and surface roughness was studied. Machinability properties of the selected material were studied using HSS and Carbide tool material; surface roughness was generally affected by feed rate and cutting speed. Hence the tool wear were measured at different speed and feed conditions. Experimental data collected are tested with Multiple Regression Analysis. On completion of the experimental test, multiple regression analysis is used to predict the wear behavior of the system under any condition within the operating range.\",\"PeriodicalId\":394515,\"journal\":{\"name\":\"2011 IEEE Recent Advances in Intelligent Computational Systems\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Recent Advances in Intelligent Computational Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAICS.2011.6069397\",\"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 Recent Advances in Intelligent Computational Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAICS.2011.6069397","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

近年来,金属基复合材料(MMC)在许多工程领域的应用显著增加。对这些复合材料精确加工的需求也大大增加了。尽管最近在近净形状制造方面有了发展,但复合材料零件通常需要模后加工以满足尺寸公差、表面质量和其他功能要求。一般来说,70%的零件需要加工才能达到最终形状。本文研究了用高速钢和硬质合金刀具对复合棒进行车削加工时刀具的磨损。本文介绍了碳化硅颗粒增强铝基复合材料切削性能的试验研究结果。研究了切削速度、进给速度和切削深度等加工参数对刀具磨损和表面粗糙度的影响。采用高速钢和硬质合金刀具材料对所选材料的切削性能进行了研究;表面粗糙度一般受进给速度和切削速度的影响。在此基础上,对不同转速和进给条件下的刀具磨损进行了测量。收集的实验数据采用多元回归分析进行检验。实验测试完成后,采用多元回归分析预测系统在工作范围内任意工况下的磨损行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tool wear prediction by Regression Analysis in turning A356 with 10% SiC
In recent years, the utilization of metal matrix composites (MMC) materials in many engineering fields has increased predominantly. The need for accurate machining of these composites has also increased enormously. Despite the recent developments in the near net shape manufacture, composite parts often require post-mold machining to meet dimensional tolerances, surface quality and other functional requirements. In general 70% of the components need machining to attain the final shape. In the present work, the tool wear has been studied in this paper by turning the composite bars using HSS and Carbide tools. The paper presents the results of experimental investigation machinability properties of silicon carbide particle (SiC-p) reinforced aluminum metal matrix composite. The effect of machining parameters, e.g. cutting speed, feed rate and depth of cut on tool wear and surface roughness was studied. Machinability properties of the selected material were studied using HSS and Carbide tool material; surface roughness was generally affected by feed rate and cutting speed. Hence the tool wear were measured at different speed and feed conditions. Experimental data collected are tested with Multiple Regression Analysis. On completion of the experimental test, multiple regression analysis is used to predict the wear behavior of the system under any condition within the operating range.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信