Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

IF 7.4 3区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Science and Technology of Advanced Materials Pub Date : 2024-08-05 DOI:10.1080/14686996.2024.2388016
Ryo Tamura, Kenji Nagata, Keitaro Sodeyama, Kensaku Nakamura, Toshiki Tokuhira, Satoshi Shibata, Kazuki Hammura, Hiroki Sugisawa, Masaya Kawamura, Teruki Tsurimoto, Masanobu Naito, Masahiko Demura, Takashi Nakanishi
{"title":"Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis","authors":"Ryo Tamura, Kenji Nagata, Keitaro Sodeyama, Kensaku Nakamura, Toshiki Tokuhira, Satoshi Shibata, Kazuki Hammura, Hiroki Sugisawa, Masaya Kawamura, Teruki Tsurimoto, Masanobu Naito, Masahiko Demura, Takashi Nakanishi","doi":"10.1080/14686996.2024.2388016","DOIUrl":null,"url":null,"abstract":"Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-orde...","PeriodicalId":21588,"journal":{"name":"Science and Technology of Advanced Materials","volume":"77 1","pages":""},"PeriodicalIF":7.4000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science and Technology of Advanced Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/14686996.2024.2388016","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-orde...
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
通过 X 射线衍射分析对注塑聚丙烯机械性能进行机器学习预测
利用机器学习预测聚合物材料的机械性能对于设计下一代聚合物至关重要。然而,高分子材料的高阶机械性能和低阶机械性能之间的密切关系,对高分子材料的机械性能预测至关重要。
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 去求助
来源期刊
Science and Technology of Advanced Materials
Science and Technology of Advanced Materials 工程技术-材料科学:综合
CiteScore
10.60
自引率
3.60%
发文量
52
审稿时长
4.8 months
期刊介绍: Science and Technology of Advanced Materials (STAM) is a leading open access, international journal for outstanding research articles across all aspects of materials science. Our audience is the international community across the disciplines of materials science, physics, chemistry, biology as well as engineering. The journal covers a broad spectrum of topics including functional and structural materials, synthesis and processing, theoretical analyses, characterization and properties of materials. Emphasis is placed on the interdisciplinary nature of materials science and issues at the forefront of the field, such as energy and environmental issues, as well as medical and bioengineering applications. Of particular interest are research papers on the following topics: Materials informatics and materials genomics Materials for 3D printing and additive manufacturing Nanostructured/nanoscale materials and nanodevices Bio-inspired, biomedical, and biological materials; nanomedicine, and novel technologies for clinical and medical applications Materials for energy and environment, next-generation photovoltaics, and green technologies Advanced structural materials, materials for extreme conditions.
期刊最新文献
Tracking the evolution of the morphology and stress distribution of SIS thermoplastic elastomers under tension using atomic force microscopy Robust and orange-yellow-emitting Sr-rich polytypoid α-SiAlON (Sr3Si24Al6N40:Eu2+) phosphor for white LEDs Multicrystalline informatics: a methodology to advance materials science by unraveling complex phenomena A comprehensive data network for data-driven study of battery materials Recent progress on polymeric probes for formaldehyde sensing: a comprehensive review.
×
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