遗传算法:解决纤维增强复合材料钻井难题的方法

S. Bhardwaj, Prof. Neeraj Bhargava, Dr. Ritu Bhargava
{"title":"遗传算法:解决纤维增强复合材料钻井难题的方法","authors":"S. Bhardwaj, Prof. Neeraj Bhargava, Dr. Ritu Bhargava","doi":"10.35940/ijese.f2548.0511623","DOIUrl":null,"url":null,"abstract":"Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.","PeriodicalId":275796,"journal":{"name":"International Journal of Emerging Science and Engineering","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genetic Algorithms: A Solution to Fiber Reinforced Composite Drilling Challenges\",\"authors\":\"S. Bhardwaj, Prof. Neeraj Bhargava, Dr. Ritu Bhargava\",\"doi\":\"10.35940/ijese.f2548.0511623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.\",\"PeriodicalId\":275796,\"journal\":{\"name\":\"International Journal of Emerging Science and Engineering\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Emerging Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.35940/ijese.f2548.0511623\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35940/ijese.f2548.0511623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

天然纤维复合材料是近年来越来越受到关注的一类材料,因为它们在各种应用中具有取代传统材料的潜力。然而,复合材料是由纤维层和树脂层组成的,它们在钻孔过程中会相互分离,导致分层。提出了一种考虑切削速度、进给速度和刀具几何形状三个关键参数的天然纤维复合材料钻孔多目标优化方法。目的是尽量减少分层和推力。采用多元线性回归分析建立各目标函数的回归方程,并利用多目标遗传算法(MOGA)对各目标函数进行同步优化。结果表明,该方法可以有效地识别出最优的钻井参数,从而平衡竞争目标之间的权衡。该方法有助于提高天然纤维复合材料的钻孔效率和质量,使其在各种工业应用中得到越来越多的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic Algorithms: A Solution to Fiber Reinforced Composite Drilling Challenges
Natural fiber composites are a group of materials that have gained increasing attention in recent years due to their potential to replace traditional materials in various applications. However composite materials are made up of layers of fibers and resin that can separate from each other during drilling, leading to delamination. This paper proposes a multi-objective optimization approach for drilling natural fiber composites, considering three key drilling parameters: cutting speed, feed rate and tool geometry. The objective is to minimize delamination and thrust force. Multiple linear regression analysis is employed to develop the regression equations for each objective function, which are then optimized simultaneously using a multi-objective genetic algorithm (MOGA). The results demonstrate that the proposed approach can effectively identify the optimal drilling parameters that balance the trade-offs between the competing objectives. The proposed approach can be useful for improving the efficiency and quality of drilling natural fiber composites, which are increasingly used in various industrial applications.
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:481959085
Book学术官方微信