Access the full text.
Sign up today, get DeepDyve free for 14 days.
T. Kurosaki, Kazuya Wada (2015)
Spatial Characteristics of Long-term Changes in Indian Agricultural Production: District-Level Analysis, 1965-2007T.H.E. Journal, 5
M. Tokarska (2004)
Neural Model of the Permeability Features of Woven FabricsTextile Research Journal, 74
Ogulata (2006)
Air permeability of woven fabrics of Textile and Apparel , Technology and ManagementJournal, 5
Ž. Zupin, A. Hladnik, K. Dimitrovski (2012)
Prediction of one-layer woven fabrics air permeability using porosity parametersTextile Research Journal, 82
I. Jasińska, Z. Stempien (2014)
An alternative instrumental method for fabric pilling evaluation based on computer image analysisTextile Research Journal, 84
Xia Chen, X. Huang (2004)
Evaluating Fabric Pilling with Light-Projected Image AnalysisTextile Research Journal, 74
B. Behera, R. Mishra (2007)
Artificial neural network‐based prediction of aesthetic and functional properties of worsted suiting fabricsInternational Journal of Clothing Science and Technology, 19
J. Militký, M. Trávníčková, V. Bajzík (1999)
Air permeability and light transmission of weavesInternational Journal of Clothing Science and Technology, 11
M. Tapias, M. Ralló, J. Escofet (2011)
Automatic measurements of partial cover factors and yarn diameters in fabrics using image processingTextile Research Journal, 81
J. Cardamone, W. Damert, J. Phillips, W. Marmer (2002)
Digital Image Analysis for Fabric AssessmentTextile Research Journal, 72
R. Ogulata (2006)
Air Permeability of Woven Fabrics
J. Szmyt, Z. Mikołajczyk (2010)
LIGHT TRANSMISSION THROUGH DECORA TIVE KNITTED FABRICS IN CORRELATION WITH THEIR FABRIC COVERAutex Research Journal, 10
A. Afzal, T. Hussain, M. Malik, Zafar Javed (2014)
Statistical model for predicting the air permeability of polyester/cotton-blended interlock knitted fabricsThe Journal of The Textile Institute, 105
Xueliang Xiao, X. Zeng, A. Long, Hua Lin, M. Clifford, E. Saldaeva (2012)
An analytical model for through-thickness permeability of woven fabricTextile Research Journal, 82
M. Tapias, M. Ralló, J. Escofet, I. Algaba, A. Riva (2010)
Objective Measure of Woven Fabric’s Cover Factor by Image ProcessingTextile Research Journal, 80
R. Turan, A. Okur (2012)
Investigation of pore parameters of woven fabrics by theoretical and image analysis methodsThe Journal of The Textile Institute, 103
Abstract The aim of this study was to develop statistical models for predicting the air permeability and light transmission properties of woven cotton fabrics and determine the level of correlation between the two parameters. Plain woven fabrics were developed with different warp and weft linear densities, ends per inch and picks per inch. After desizing, scouring, bleaching, drying and conditioning, the air permeability and light transmission properties of the fabric samples were determined. Regression analysis results showed statistically significant effect of the fabric ends, picks and warp linear density on both the fabric air permeability and light transmission. Correlation analysis was performed to analyze the relation between the fabric air permeability and light transmission. A linear equation was also formulated to find the fabric air permeability through transmission of light intensity. A fitted line plot between the air permeability and light transmission exhibited significant correlation with R-sq. value of 96.4%. The statistical models for the prediction of fabric air permeability and light transmittance were developed with an average prediction error of less than 7%.
Autex Research Journal – de Gruyter
Published: Mar 1, 2017
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.