Tensile Properties and Static Fatigue Behavior of Cotton Warp Yams

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Warp yams under a state of dynamic loading on a loom have to withstand cumulative damage caused by tensile fatigue at relatively low loads coupled with the abrasive action of the machine parts. To evaluate the fatigue phenomenon of sized cotton yams and its correlation with performance on a loom, one must first understand basic mechanical properties of yams in terms of fiber properties and fiber length distribution. The first section of this paper reports on the effect of fiber length distribution on tensile properties of yams at various tensile strain rates, and the second section presents the . fatigue behavior of 100% cotton yams. Fiber length and its distribution in cotton yams seem to influence the tensile behavior of both sized and unsized yams at all strain rates, but strain rates do not play a significant role in determining the properties of sized yams. Adding size tends to mask the effect of fiber length variation, particularly at higher size loading. The tensile properties of sized cotton yams are also influenced by the size type, such as polyester, carboxymethyl cellulose, and polyvinyl alcohol, at all add-on levels. The fatigue behavior of 100% cotton unsized and sized yam is eval uated using a Sulzer-Ruti Webtester. The mechanism of yam failure due to fatigue/ abrasion for unsized yam is affected by short fiber content. The base tension has the largest overall effect. Unsized yam containing 10% comber noil has an optimum bal ance of short and long fibers, making the yam structure more resistant to fatigue / abrasion. In sized yam, the effect of short fibers is partly masked by the properties and compatibility of size materials with the substrate. Polyester size has poor com patibility with cotton fibers; carboxymethyl cellulose and partially hydrolyzed polyvinyl alcohol tend to produce very highly increased resistance to fatigue and abrasion. Rep resenting fatigue /abrasion data in terms of mean or average lifetimes is questionable due to contradictory evidence. Representing fatigue data in terms of a three-parameter Weibull distribution leads to realistic interpretations.