Application of Neural Network for the Prediction of Loss in Mechanical Properties of Aramid Fabrics After Thermal Aging

IF 0.6 4区 工程技术 Q4 MATERIALS SCIENCE, TEXTILES
Banu Özgen Keleş
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引用次数: 0

Abstract

Aramid fabrics are used to produce most of the flame resistant protection clothes to fulfil the protection requirements. Even though aramid fibers have good thermal stability and flame resistance properties, fabrics used in protective clothing age and loss some of their essential functions under various environmental and operational conditions during their lifetime. These conditions cause serious limitations in the use of clothing. In this study, various woven fabrics produced from aramid (Nomex, Kevlar) fabrics were exposed to accelerated aging tests under varying temperature and time period in order to construct Neural Network models to predict weight loss and tensile strength loss percentages of the fabrics. The results of Artificial Neural Network models demonstrate that regression values are 0.98405 for weight loss percentages and 0.99935 for tensile strength loss percentages of the fabrics. Accordingly, the proposed Artificial Neural Network models are correctly constituted and the losses in determined fabric properties is successfully predicted.
神经网络在芳纶织物热老化后力学性能损失预测中的应用
大部分阻燃防护服采用芳纶面料,以满足防护要求。尽管芳纶纤维具有良好的热稳定性和阻燃性能,但用于防护服的织物在其使用寿命期间,在各种环境和操作条件下会老化并失去一些基本功能。这些情况严重限制了衣物的使用。本研究以芳纶(Nomex)、凯夫拉(Kevlar)织物为原料,对不同温度、不同时间的机织物进行加速老化试验,建立神经网络模型,预测织物的失重率和抗拉强度损失率。人工神经网络模型的回归结果表明,织物的失重率回归值为0.98405,拉伸强度损失率回归值为0.99935。在此基础上,建立了正确的人工神经网络模型,成功地预测了织物性能的损失。
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来源期刊
Tekstil Ve Konfeksiyon
Tekstil Ve Konfeksiyon 工程技术-材料科学:纺织
CiteScore
1.40
自引率
33.30%
发文量
41
审稿时长
>12 weeks
期刊介绍: Tekstil ve Konfeksiyon, publishes papers on both fundamental and applied research in various branches of apparel and textile technology and allied areas such as production and properties of natural and synthetic fibers, yarns and fabrics, technical textiles, finishing applications, garment technology, analysis, testing, and quality control.
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