基于聚类搜索算法的制造单元的形成

P.H. Gu, H.A. ElMaraghy
{"title":"基于聚类搜索算法的制造单元的形成","authors":"P.H. Gu,&nbsp;H.A. ElMaraghy","doi":"10.1016/0378-3804(89)90048-X","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.</p></div>","PeriodicalId":100801,"journal":{"name":"Journal of Mechanical Working Technology","volume":"20 ","pages":"Pages 403-413"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0378-3804(89)90048-X","citationCount":"5","resultStr":"{\"title\":\"Formation of manufacturing cells by cluster-seeking algorithms\",\"authors\":\"P.H. Gu,&nbsp;H.A. ElMaraghy\",\"doi\":\"10.1016/0378-3804(89)90048-X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.</p></div>\",\"PeriodicalId\":100801,\"journal\":{\"name\":\"Journal of Mechanical Working Technology\",\"volume\":\"20 \",\"pages\":\"Pages 403-413\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0378-3804(89)90048-X\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Working Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/037838048990048X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Working Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/037838048990048X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

本文提出了三种聚类搜索算法- K-means、修正K-means和Isodata -用于零件族和机器单元的形成。这些算法基于模式识别的概念,能够生产不同尺寸、相互独立的零件和/或机器组,而不排除特殊组件。将这些算法与现有的分组算法进行了比较,并用实例说明了聚类准则对最终解的影响。研究发现,Isodata算法比现有的机器/组件矩阵操作技术更有效、更灵活。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Formation of manufacturing cells by cluster-seeking algorithms

This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.

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
期刊最新文献
Editorial Board Author index Thermal-based tool sensor for ball burnishing Measurement of temperature distribution within tool in metal cutting. Experimental tests and numerical analysis Influence of die guidance systems on the angular deflection of press slide and die under eccentric loading
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1