منابع مشابه
Bending and Compression Behaviour of Polyester Air-jet-textured and Cotton-yarn Fabrics
Air-jet-textured polyester yarns were produced using two feed yarns differing in filament fineness and number of filaments. By varying the yarn overfeed, filament fineness and air pressure, four textured yarns were produced. Woven fabrics were prepared using these textured yarns as weft and cotton yarns in warp. To study the effect of air-jet-texturing parameters on the bending and compression ...
متن کاملCotton Yarn Engineering Via Fuzzy Least Squares Regression
Modeling of yarn and fiber properties has been a popular topic in the field of textile engineering in recent decades. The common method for fitting models has been to use classical regression analysis, based on the assumptions of data crispness and deterministic relations among variables. However, in modeling practical systems such as cotton spinning, the above assumptions may not hold true. Pr...
متن کاملImproving light fastness of natural dyes on cotton yarn
The objectives of this study were to evaluate the light fastness of selected natural dyes (madder, weld and woad) and the effect of some commonly used antioxidants and UV absorbers on the light fastness of these dyes. The photofading rate curves of madder and weld fixed on cotton correspond to type II fading rate curves described by Giles. These results are in concordance with those of Cox-Crew...
متن کاملFiber and Yarn Properties Improve with New Cotton Cultivar
J. Foulk, USDA-ARS Cotton Quality Research Station (CQRS), Ravenel Center Room 10, McGregor Road, Clemson, SC 29634;W. Meredith, USDA-ARS Crop Genetics and Production Research Unit (CPGPRU), Cotton Physiology & Genetics, P.O. Box 314, Stoneville, MS 38776; D. McAlister, Uster Technologies, Inc., 456 Troy Circle, Knoxville, TN 37919; D. Luke, Cooper Power Systems, 1520 Emerald Rd, Greenwood, SC ...
متن کاملForecasting of Cotton Yarn Properties Using Intelligent Machines
An intelligence machine is a computer program that can learn from experience, i.e. modifies its processing on the basis of newly acquired information and thereafter makes decisions in a rightfully sensible manner when presented with inputs. Examples of such machine learning systems are artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic, evolutionary computation, etc....
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ژورنال
عنوان ژورنال: Sen'i Kikai Gakkaishi (Journal of the Textile Machinery Society of Japan)
سال: 1999
ISSN: 0371-0580,1880-1994
DOI: 10.4188/transjtmsj.52.7_p307