Abstract
The primary objective of this study is to investigate the bagging behavior of knitted fabrics produced from various fiber blends-namely recycled cotton/recycled polyester, recycled cotton/polyester, cotton/polyester, recycled cotton/acrylic, and cotton/acrylic-and to evaluate their suitability for home wear applications. The study further aims to compare these fabrics with those produced from virgin yarns and to model their bagging properties using artificial neural networks (ANNs). A total of 15 knitted fabric samples were produced using both recycled and virgin yarns in three different fabric structures: single jersey, rib, and interlock. The bagging performance of the fabrics was assessed through key parameters, including bagging hysteresis percentage, bagging fatigue percentage, residual bagging hysteresis percentage, and bagging resistance. Statistical analysis of the test results revealed that, depending on the yarn composition and fabric structure, the incorporation of recycled fibers in yarns led to improvements in certain bagging properties. These improvements were observed across all fabric types and knitting structures. Additionally, the ANN-based modeling demonstrated high accuracy in predicting the bagging behavior of recycled knitted fabrics, thereby offering a valuable tool for optimizing fabric design before production.
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