A novel systematic approach for robust numerical simulation of carbon fiber-reinforced plastic circular tubes: Utilizing machine-learning techniques for calibration and validation

IF 2.3 3区 材料科学 Q3 MATERIALS SCIENCE, COMPOSITES
Milad Abbasi, Abolfazl Khalkhali, Johannes Sackmann
{"title":"A novel systematic approach for robust numerical simulation of carbon fiber-reinforced plastic circular tubes: Utilizing machine-learning techniques for calibration and validation","authors":"Milad Abbasi, Abolfazl Khalkhali, Johannes Sackmann","doi":"10.1177/00219983241241304","DOIUrl":null,"url":null,"abstract":"Developing a reliable and robust finite element model of a carbon fiber-reinforced plastic (CFRP) composite structure is investigated by using the LS-DYNA solver and Python. This study tries to provide a systematic numerical approach to cover the principal impediment to adaptation of composite energy absorbers, that is the lack of a reliable predictive method. The proposed procedure aims to further the understanding of advanced composite structures’ behavior during the crash phenomenon by developing an accurate finite element model. To do so, the mechanical properties of the material were extracted from American Society for Testing and Materials (ASTM) standard test methods, followed by experimental investigation of circular CFRP tubes undergoing quasi-static loading. A numerical simulation framework was then utilized to scrutinize the effectiveness of simulation parameters on the crushing mechanism. Finally, a systematic approach based on machine learning techniques was performed to adjust non-physical modeling parameters for further calibration and validation. In this regard, a versatile Python code was developed to automate pre-processing, processing, and post-processing steps. The code also provides a groundwork to perform machine learning techniques. Interestingly, the numerical and experimental results were highly correlated with a correlation coefficient of almost 90%. Additionally, several non-physical numerical parameters were found to be inactive, while some else were identified as effective parameters, and their corresponding effectiveness was quantitatively extracted and discussed for the first time in the literature.","PeriodicalId":15489,"journal":{"name":"Journal of Composite Materials","volume":"260 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Composite Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1177/00219983241241304","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
引用次数: 0

Abstract

Developing a reliable and robust finite element model of a carbon fiber-reinforced plastic (CFRP) composite structure is investigated by using the LS-DYNA solver and Python. This study tries to provide a systematic numerical approach to cover the principal impediment to adaptation of composite energy absorbers, that is the lack of a reliable predictive method. The proposed procedure aims to further the understanding of advanced composite structures’ behavior during the crash phenomenon by developing an accurate finite element model. To do so, the mechanical properties of the material were extracted from American Society for Testing and Materials (ASTM) standard test methods, followed by experimental investigation of circular CFRP tubes undergoing quasi-static loading. A numerical simulation framework was then utilized to scrutinize the effectiveness of simulation parameters on the crushing mechanism. Finally, a systematic approach based on machine learning techniques was performed to adjust non-physical modeling parameters for further calibration and validation. In this regard, a versatile Python code was developed to automate pre-processing, processing, and post-processing steps. The code also provides a groundwork to perform machine learning techniques. Interestingly, the numerical and experimental results were highly correlated with a correlation coefficient of almost 90%. Additionally, several non-physical numerical parameters were found to be inactive, while some else were identified as effective parameters, and their corresponding effectiveness was quantitatively extracted and discussed for the first time in the literature.
对碳纤维增强塑料圆管进行稳健数值模拟的新型系统方法:利用机器学习技术进行校准和验证
通过使用 LS-DYNA 求解器和 Python,研究了如何为碳纤维增强塑料(CFRP)复合材料结构开发可靠、稳健的有限元模型。本研究试图提供一种系统的数值方法,以解决复合材料吸能器适应性的主要障碍,即缺乏可靠的预测方法。所提出的程序旨在通过开发精确的有限元模型,进一步了解先进复合材料结构在碰撞现象中的行为。为此,我们从美国材料与试验协会(ASTM)的标准测试方法中提取了材料的机械性能,然后对承受准静态加载的圆形 CFRP 管进行了实验研究。然后,利用数值模拟框架仔细研究模拟参数对挤压机制的影响。最后,采用基于机器学习技术的系统方法来调整非物理建模参数,以便进一步校准和验证。为此,我们开发了一套通用的 Python 代码,用于自动执行预处理、处理和后处理步骤。该代码还为执行机器学习技术提供了基础。有趣的是,数值结果和实验结果高度相关,相关系数接近 90%。此外,还发现一些非物理数值参数不起作用,而另一些则被确定为有效参数,并在文献中首次对其相应的有效性进行了定量提取和讨论。
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
Journal of Composite Materials
Journal of Composite Materials 工程技术-材料科学:复合
CiteScore
5.40
自引率
6.90%
发文量
274
审稿时长
6.8 months
期刊介绍: Consistently ranked in the top 10 of the Thomson Scientific JCR, the Journal of Composite Materials publishes peer reviewed, original research papers from internationally renowned composite materials specialists from industry, universities and research organizations, featuring new advances in materials, processing, design, analysis, testing, performance and applications. This journal is a member of the Committee on Publication Ethics (COPE).
×
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