Enhancing the structural performance of engineering components using the geometric mean optimizer

IF 2.4 4区 材料科学 Q2 MATERIALS SCIENCE, CHARACTERIZATION & TESTING
Pranav Mehta, Ali Rıza Yıldız, S. M. Sait, B. Yildiz
{"title":"Enhancing the structural performance of engineering components using the geometric mean optimizer","authors":"Pranav Mehta, Ali Rıza Yıldız, S. M. Sait, B. Yildiz","doi":"10.1515/mt-2024-0005","DOIUrl":null,"url":null,"abstract":"\n In this article, a newly developed optimization approach based on a mathematics technique named the geometric mean optimization algorithm is employed to address the optimization challenge of the robot gripper, airplane bracket, and suspension arm of automobiles, followed by an additional three engineering problems. Accordingly, other challenges are the ten-bar truss, three-bar truss, tubular column, and spring systems. As a result, the algorithm demonstrates promising statistical outcomes when compared to other well-established algorithms. Additionally, it requires less iteration to achieve the global optimum solution. Furthermore, the algorithm exhibits minimal deviations in results, even when other techniques produce better or similar outcomes. This suggests that the proposed approach in this paper can be effectively utilized for a wide range of critical industrial and real-world engineering challenges.","PeriodicalId":18231,"journal":{"name":"Materials Testing","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Testing","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1515/mt-2024-0005","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

Abstract

In this article, a newly developed optimization approach based on a mathematics technique named the geometric mean optimization algorithm is employed to address the optimization challenge of the robot gripper, airplane bracket, and suspension arm of automobiles, followed by an additional three engineering problems. Accordingly, other challenges are the ten-bar truss, three-bar truss, tubular column, and spring systems. As a result, the algorithm demonstrates promising statistical outcomes when compared to other well-established algorithms. Additionally, it requires less iteration to achieve the global optimum solution. Furthermore, the algorithm exhibits minimal deviations in results, even when other techniques produce better or similar outcomes. This suggests that the proposed approach in this paper can be effectively utilized for a wide range of critical industrial and real-world engineering challenges.
利用几何平均优化器提高工程部件的结构性能
本文采用了一种新开发的优化方法,该方法基于一种名为几何平均优化算法的数学技术,用于解决机器人抓手、飞机支架和汽车悬挂臂的优化难题,随后又解决了另外三个工程问题。相应地,其他难题包括十杆桁架、三杆桁架、管柱和弹簧系统。因此,与其他成熟的算法相比,该算法的统计结果很有前途。此外,该算法只需较少的迭代次数即可获得全局最优解。此外,即使其他技术产生了更好或类似的结果,该算法的结果偏差也很小。这表明,本文提出的方法可以有效地用于应对各种关键的工业和实际工程挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Materials Testing
Materials Testing 工程技术-材料科学:表征与测试
CiteScore
4.20
自引率
36.00%
发文量
165
审稿时长
4-8 weeks
期刊介绍: Materials Testing is a SCI-listed English language journal dealing with all aspects of material and component testing with a special focus on transfer between laboratory research into industrial application. The journal provides first-hand information on non-destructive, destructive, optical, physical and chemical test procedures. It contains exclusive articles which are peer-reviewed applying respectively high international quality criterions.
×
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