Compact or condensed spinning technology is widely considered as the new benchmark for staple yarn quality. The enhanced structure of compact yarn typically results in a lower hairiness and improved mechanical properties. The present study examines these two key benefits of compact technology when applied to short-to-medium staple cotton. The main focus is on interaction effects involving various raw fiber properties rather than on the overall effects. The results show that, with some combinations of fiber characteristics, using the compact technology does not lead to significant hairiness reduction. However, yarn tensile properties (strength and elongation values) do not appear to be directly affected by these interactions.

Models for predicting ring or rotor yarn hairiness are built using a back-propagation neural network algorithm. These models are based on fiber property input measured by three different systems, hvi, afis, and fmt. We compare the prediction results from the different models, which reveal that yarn hairiness measurements from hvi data are superior to other models. The optimum model is based on the availability of all three measurement systems. We also study the impact of each fiber property on yarn hairiness. The dominant effect is fiber length. Each of the remaining properties has a different degree of impact on ring or rotor yarn hairiness.

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Using samples from 96 bales of Texas High Plains cotton, multiple regression techniques were used to select the "best" functional expression for the impacts of High-Volume-Instrument-measured fiber properties on the strength of open-end spun yarns. Results indicate that the most important fiber properties are strength, fineness, and length uniformity. Impacts of fiber properties are typically nonlinear. The micronaire value is inversely related to yarn strength and is best expressed as a cubic polynomial.. ABSTRACTThe development of the Cottonmaster™ fiber cleaner, its construction, strengths, and limitations are discussed. Data from several studies are used to compare the Cottonmaster's performance with regular cleaning machinery on several kinds of cotton fibers, yarns, and fabrics.

This paper presents a neural network model for predicting the yarn irregularity, based on inputs of fiber property measurements with the AFIS instrument. By using a back-propagation neural network algorithm, alternative models were fitted and compared. The resulting predictions of yarn irregularity are superior to these obtained by using conventional multiplelinear regression techniques.• I