A New Approach to Predicting Cotton Yarn Quality Attributes from Fiber Characteristics Using Universal Equations-Part II- Update and Merits

Yehia Elmogahzy, and Ramsis Farag

This paper represents Part II of a project aiming at developing reliable fiber-to-yarn models that can predict or, more specifically estimate, yarn quality parameters from a given set of fiber properties. The models aim at serving both cotton producers and cotton users in such a way that can truly assist in improving the quality and the value of US cotton. From a cotton-producer viewpoint, the models should be capable of providing distinct differences between the quality values of different varieties (or types) of cottons and the relative contributions of different fiber properties with respect to particular spinning systems and fabric styles. These values are critical for improvements in breeding, growing, and ginning. From a cotton-user viewpoint, the value of a certain cotton stems from two critical factors: (a) fiber cost, and (b) fiber contribution to the value of yarn or fabric (quality, image, and price). Fiber-to-yarn models can certainly assist in these two critical areas provided that they are reliable, verifiable, and physically correct. The approach presented is new as it is based on estimation of ranges of yarn quality attributes (from worst to best values). Companies utilizing US cotton can adapt the developed models for the benefits of estimating possible ranges of yarn attributes produced from US cottons and comparing their actual performance with the outcomes of these models. In this part, an updated report on the progress of this important project will be presented.