4.5 Article

Prediction of one-layer woven fabrics air permeability using porosity parameters

期刊

TEXTILE RESEARCH JOURNAL
卷 82, 期 2, 页码 117-128

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/0040517511424529

关键词

Woven fabrics; number of pores; hydraulic diameter of pores; porosity; air permeability; principal components analysis; multiple linear regression

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Air permeability is one of the fundamental textile properties influencing the design of comfortable clothes. In particular, it is very important in the field of technical textiles. Air permeability depends mainly on the fabric structure, which can be described by yarn linear density, type of yarn, warp/weft density and weave. The purpose of our study was to identify a small number of parameters that have the strongest influence on air permeability of cotton fabrics and enable its good prediction. Rather than focusing on the constructional parameters, we decided to include a composite parameter known from the theory of fluids, hydraulic diameter of pores, which treats rectangular-shaped pores as circular ones. In addition to the hydraulic diameter of pores, two other parameters were used for the prediction of air permeability: the number of macro pores and the total porosity of woven fabrics. 36 woven fabric samples were produced using nine frequently implemented weave types together with two warp densities (29.3 and 22 ends/cm) and two weft densities (15 and 20 picks/cm), resulting in four different densities of woven fabrics. The yarns had the same linear density and material in warp and weft directions. Air permeability measurements were performed with the Air Permeability tester FX 3300 Labotester III (Textest Instruments) according to the ISO 9237:1995 (E) standard. Principal components analysis revealed that the four investigated plain weave specimens behave differently than the other samples, which might be explained by weave structure. This multivariate statistical method also confirmed the appropriateness of the three selected parameters for air permeability prediction which was done using multiple linear regression. The high adjusted coefficient of determination (R-2) value of 0.94 indicates that the model explains variability in the air permeability to a large extent.

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