Prediction of the Porosity of Barrier Woven Fabrics with Respect to Material, Construction and Processing Parameters and Its Relation with Air Permeability
Prediction of the Porosity of Barrier Woven Fabrics with Respect to Material, Construction and Processing Parameters and Its Relation with Air Permeability
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DOI：
10.5604/01.3001.0011.7306
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发表时间：
2018-05-01
影响因子：
0.9
通讯作者：
Cherif, Chokri
中科院分区：
文献类型：
--
作者：
Malik, Samander Ali;Kocaman, Recep Turkay;Cherif, Chokri
孔隙率是织物最重要的特性之一，决定织物的渗透性和保留性能。多种技术用途要求纺织品具有一定的渗透性和保留性能。除了过滤之外，手术纺织品还需要这些相反的特性（或有效阻挡含有颗粒的液体，例如细菌和病毒），同时增加佩戴者的舒适度。孔径和孔径分布是通过影响有效孔隙率来确定多丝屏障纺织品的渗透性和保留行为的重要特征，有效孔隙率可以根据材料、编织结构和加工因素的最终使用要求进行定制。本研究的目的是 开发材料、结构和织机参数与孔隙率（即织物的平均孔径和平均流量孔径）之间的关系，从而与透气率之间的关系。为了绘制这种非线性复杂关系，采用了人工神经网络（ANN）。从这些发现中可以看出，使用人工神经网络可以非常准确地预测阻隔织物的孔隙率。
Porosity is one of the most mast important characteristics of fabrics that dictate the permeability and retention properties of fabrics. Several technical uses require textiles with a combination of definite permeability and retention properties. Besides filtration, surgical textiles require these contrary properties to (Or an effective harrier against particle laden fluids, such as bacteria and viruses, together with added wearer comfort. Pore size and pore size distribution are important characteristics to determine the permeability and retention behaviour of multifilament barrier textiles by influencing the effective porosity, which can be tailored according to end use requirements by material, weave construction and processing factors. The present research was aimed at developing the relationship that material, construction and loom parameters have with porosity in terms of the mean pore size and mean flow pore size of the fabric, and thereby with air permeability To map such nonlinear complex relations, an artificial neural network (ANN) was employed. From the findings, it was observed that the porosity of barrier fabrics can be predicted with excellent accuracy using an ANN.