贯叶连翘真空等离子体处理真丝织物的天然染色及染色特性的可优化神经网络模型估计

IF 2.2 4区 工程技术 Q1 MATERIALS SCIENCE, TEXTILES
Can Eyupoglu, Seyda Eyupoglu, Nigar Merdan, Zeynep Omerogullari Basyigit
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引用次数: 0

摘要

研究了空气等离子体预处理对真丝织物可染性的影响。对不同暴露时间的脱胶和生丝织物样品进行空气等离子体预处理,以修饰织物表面,使染色过程更环保,更具可持续性。以贯叶连翘(hypericum perforatum)为原料,采用生态微波辅助染色法对真丝织物进行染色。为了确定等离子体预处理对真丝织物样品的影响,进行了扫描电镜和傅里叶变换红外分析。此外,还研究了等离子体曝光和染色时间对染料比色和牢度性能的影响。采用扫描电镜分析方法测定了等离子体预处理对真丝织物样品的腐蚀效果。实验结果表明,等离子体预处理、等离子体曝光时间和染色时间对染料的牢度和比色特性都有影响。样品的颜色强度随着脱胶工艺和等离子体处理的增加而增加。随着等离子体暴露时间的增加,原丝织物样品的颜色变化从3-4提高到4。原丝织物样品经等离子体处理后,摩擦牢度可达5。对于脱胶真丝织物样品,等离子体处理后牢度性能没有明显改善。本文提出并实现了一种基于贝叶斯优化器的可优化神经网络(ONN)模型,用于预测真丝织物的染色特性,包括湿、干摩擦牢度、色变、L、a、b和K/S。使用r平方(R2)、平均绝对误差(MAE)、均方根误差(RMSE)和均方误差(MSE)指标来评估所提出模型在预测染色特性方面的成功性。实验结果表明,该模型能较好地预测真丝织物的染色性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Natural Dyeing of Vacuum Plasma-Treated Silk Fabric with Hypericum Perforatum and Estimation of Dyeing Characteristics with an Optimizable Neural Network Model

This study examines the effects of air plasma pre-treatment on the dyeability behavior of silk fabric. Air plasma pre-treatment was applied to both degummed and raw silk fabric samples with different exposure times to modify the fabric surface and make the dyeing process greener and more sustainable. The silk fabric samples were dyed with the natural dye extracted from tipton weed (hypericum perforatum) using an ecological microwave-assisted method. Due to determining the effect of plasma pre-treatment on silk fabric samples, scanning electron microscope and Fourier-transform infrared analysis was achieved. Furthermore, the effect of plasma exposure and dyeing time on colorimetric and fastness properties was investigated. The etching effect of plasma pre-treatment on silk fabric samples was determined using scanning electron microscopic analysis. The experimental results show that plasma pre-treatment, plasma exposure time, and dyeing time affected fastness and colorimetric characteristics. The color strength of samples increased with the degummed process and plasma treatment. The color change of samples improved from 3–4 to 4 with an increase in plasma exposure time for raw silk fabric samples. Rubbing fastness of raw silk fabric samples rose to 5 with plasma treatment. For degummed silk fabric samples, significant improvements in fastness properties have not been seen after plasma treatment. In this study, an optimizable neural network (ONN) model with a Bayesian optimizer was proposed and implemented for predicting the dyeing characteristics of silk fabrics, which are wet and dry rubbing fastness, color change, L, a, b, and K/S. The R-squared (R2), mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) metrics were used to evaluate the success of the proposed model in terms of predicting the dyeing characteristics. Experimental results indicate that the proposed ONN model is successful in predicting the dyeing properties of silk fabrics.

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来源期刊
Fibers and Polymers
Fibers and Polymers 工程技术-材料科学:纺织
CiteScore
3.90
自引率
8.00%
发文量
267
审稿时长
3.9 months
期刊介绍: -Chemistry of Fiber Materials, Polymer Reactions and Synthesis- Physical Properties of Fibers, Polymer Blends and Composites- Fiber Spinning and Textile Processing, Polymer Physics, Morphology- Colorants and Dyeing, Polymer Analysis and Characterization- Chemical Aftertreatment of Textiles, Polymer Processing and Rheology- Textile and Apparel Science, Functional Polymers
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