Prediction of Polyester/Cotton Ring Spun Yarn Unevenness Using Adaptive Neuro Fuzzy Inference System

Samson Rwawiire, Allan Kasedde, Ildephonse Nibikora, George Wandera

Abstract


Yarn produced from a series of experiments carried out at Southern Range Nyanza Limited (SRNL) in Jinja – Uganda was used in developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model to probe the yarn unevenness of a polyester/cotton (65:35) blend. Blending was carried out at the draw frame. Parameters which are functions of yarn unevenness such as yarn count, spindle speeds and yarn twist were used as inputs for the ANFIS model. Coefficient of Variation (CV%) was used as a measure of yarn unevenness, the output of the model. The model had an R-square (R2) of 0.86, Root mean square error (RMSE) of 0.65 and SSE of 10.86, therefore rending the ANFIS model a success and superior to linear regression methods in predicting polyester/cotton yarn unevenness.

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