机织物力学性能的人工神经网络预测

IF 0.7 4区 工程技术 Q3 MATERIALS SCIENCE, TEXTILES Fibres & Textiles in Eastern Europe Pub Date : 2022-07-01 DOI:10.2478/ftee-2022-0036
Sherien N. Elkateb
{"title":"机织物力学性能的人工神经网络预测","authors":"Sherien N. Elkateb","doi":"10.2478/ftee-2022-0036","DOIUrl":null,"url":null,"abstract":"Abstract This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.","PeriodicalId":12309,"journal":{"name":"Fibres & Textiles in Eastern Europe","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Prediction of Mechanical Properties of Woven Fabrics by ANN\",\"authors\":\"Sherien N. Elkateb\",\"doi\":\"10.2478/ftee-2022-0036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.\",\"PeriodicalId\":12309,\"journal\":{\"name\":\"Fibres & Textiles in Eastern Europe\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fibres & Textiles in Eastern Europe\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.2478/ftee-2022-0036\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, TEXTILES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fibres & Textiles in Eastern Europe","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.2478/ftee-2022-0036","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, TEXTILES","Score":null,"Total":0}
引用次数: 3

摘要

摘要本研究旨在获得一个准确的机织物力学性能预测模型,以达到客户满意度。用不同支数和纬纱棉与涤纶在不同纬纱密度下的混纺比例生产平纹织物样品。测试了拉伸强度、弯曲刚度和经纬向伸长率等力学性能。预测模型是基于人工神经网络的。对于每个模型,三十九个样本用于训练,十五个样本用于测试预测性能。研究结果表明,人工神经网络在预测所有特性方面都取得了完美的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Prediction of Mechanical Properties of Woven Fabrics by ANN
Abstract This study aims to obtain an accurate prediction model of mechanical properties of woven fabric to achieve customer satisfaction. Samples of plain woven fabric were produced from different yarn counts and blend ratios of cotton and polyester of weft yarn at different weft densities. Mechanical properties such as tensile strength, bending stiffness and elongation% in both the warp and weft directions were tested. The prediction model was based on Artificial Neural Networks (ANNs). For each model, thirty-nine samples were used for training and fifteen for testing prediction performance. Findings indicated that the ANN achieved a perfect performance in predicting all properties.
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
Fibres & Textiles in Eastern Europe
Fibres & Textiles in Eastern Europe 工程技术-材料科学:纺织
CiteScore
1.60
自引率
11.10%
发文量
12
审稿时长
13.5 months
期刊介绍: FIBRES & TEXTILES in Eastern Europe is a peer reviewed bimonthly scientific journal devoted to current problems of fibre, textile and fibrous products’ science as well as general economic problems of textile industry worldwide. The content of the journal is available online as free open access. FIBRES & TEXTILES in Eastern Europe constitutes a forum for the exchange of information and the establishment of mutual contact for cooperation between scientific centres, as well as between science and industry.
期刊最新文献
Investigation of the Accelerated Ageing of Carbon-Epoxy Composites on their Mechanical Properties Knitted Heating Mats Evaluation of Insulation against Contact Heat, Radiant Heat and Sensory Comfort of Basalt Fabric-Based Composites with Parylene C Coating Digital Inheritance of Traditional Mongolian Robes of the Nayman Tribe Experimental Investigation on the Bending Behavior of Weft Interlaced Multilayered Woven Fabrics for Composite Applications
×
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