基于机器学习的搅拌铸造碳化硅增强镁基复合材料拉伸强度研究

IF 2.9 2区 材料科学 Q2 METALLURGY & METALLURGICAL ENGINEERING
Zhihong Zhu, Wenhang Ning, Xuanyang Niu, Yuhong Zhao
{"title":"基于机器学习的搅拌铸造碳化硅增强镁基复合材料拉伸强度研究","authors":"Zhihong Zhu,&nbsp;Wenhang Ning,&nbsp;Xuanyang Niu,&nbsp;Yuhong Zhao","doi":"10.1007/s40195-024-01673-5","DOIUrl":null,"url":null,"abstract":"<div><p>SiC is the most common reinforcement in magnesium matrix composites, and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC. Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process. However, due to the numerous adjustable parameters, using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material. Therefore, this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process. By analyzing the influence of SiC parameters and processing parameters on composite material performance, we have established an effective predictive model. Furthermore, six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites. Through validation and comparison, our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material. The research findings indicate that hot extrusion treatment, SiC content, and stirring time have a significant impact on the tensile strength.</p></div>","PeriodicalId":457,"journal":{"name":"Acta Metallurgica Sinica-English Letters","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting\",\"authors\":\"Zhihong Zhu,&nbsp;Wenhang Ning,&nbsp;Xuanyang Niu,&nbsp;Yuhong Zhao\",\"doi\":\"10.1007/s40195-024-01673-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>SiC is the most common reinforcement in magnesium matrix composites, and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC. Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process. However, due to the numerous adjustable parameters, using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material. Therefore, this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process. By analyzing the influence of SiC parameters and processing parameters on composite material performance, we have established an effective predictive model. Furthermore, six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites. Through validation and comparison, our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material. The research findings indicate that hot extrusion treatment, SiC content, and stirring time have a significant impact on the tensile strength.</p></div>\",\"PeriodicalId\":457,\"journal\":{\"name\":\"Acta Metallurgica Sinica-English Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Metallurgica Sinica-English Letters\",\"FirstCategoryId\":\"1\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s40195-024-01673-5\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Metallurgica Sinica-English Letters","FirstCategoryId":"1","ListUrlMain":"https://link.springer.com/article/10.1007/s40195-024-01673-5","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

碳化硅是镁基复合材料中最常见的增强材料,碳化硅增强镁基复合材料的拉伸强度与碳化硅的分布密切相关。要实现 SiC 的均匀分布,需要对 SiC 的参数以及加工和制备过程进行精细控制。然而,由于可调参数较多,使用传统实验方法需要进行大量实验才能获得均匀分布的复合材料。因此,本研究采用机器学习方法来探索 SiC 增强镁基复合材料在机械搅拌铸造过程中的拉伸强度。通过分析 SiC 参数和加工参数对复合材料性能的影响,我们建立了一个有效的预测模型。此外,我们还建立了六个不同的机器学习回归模型来预测 SiC 增强镁基复合材料的拉伸强度。通过验证和比较,我们的模型在预测复合材料拉伸强度方面表现出良好的准确性和可靠性。研究结果表明,热挤压处理、SiC 含量和搅拌时间对拉伸强度有显著影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting

Machine Learning-Based Research on Tensile Strength of SiC-Reinforced Magnesium Matrix Composites via Stir Casting

SiC is the most common reinforcement in magnesium matrix composites, and the tensile strength of SiC-reinforced magnesium matrix composites is closely related to the distribution of SiC. Achieving a uniform distribution of SiC requires fine control over the parameters of SiC and the processing and preparation process. However, due to the numerous adjustable parameters, using traditional experimental methods requires a considerable amount of experimentation to obtain a uniformly distributed composite material. Therefore, this study adopts a machine learning approach to explore the tensile strength of SiC-reinforced magnesium matrix composites in the mechanical stirring casting process. By analyzing the influence of SiC parameters and processing parameters on composite material performance, we have established an effective predictive model. Furthermore, six different machine learning regression models have been developed to predict the tensile strength of SiC-reinforced magnesium matrix composites. Through validation and comparison, our models demonstrate good accuracy and reliability in predicting the tensile strength of the composite material. The research findings indicate that hot extrusion treatment, SiC content, and stirring time have a significant impact on the tensile strength.

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
Acta Metallurgica Sinica-English Letters
Acta Metallurgica Sinica-English Letters METALLURGY & METALLURGICAL ENGINEERING-
CiteScore
6.60
自引率
14.30%
发文量
122
审稿时长
2 months
期刊介绍: This international journal presents compact reports of significant, original and timely research reflecting progress in metallurgy, materials science and engineering, including materials physics, physical metallurgy, and process metallurgy.
×
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