Development of predictive model for setting stitch length value of single jersey cotton fabrics

Abstract

A statistical approach has been used to predict the stitch length of single jersey fabrics from known yarn counts and fabric area densities in the grey reference state. The model has been based on the observational data from two hundred and sixty samples of tubular weft-knitted single jersey cotton fabrics produced under bulk conditions in a knitting plant. The multiple regression analysis technique is used to develop the predictive equation. Validation of the model by follow up on knitted samples reveals that the predicted stitch length value from the equation is acceptably close to the real value. The statistical model can therefore be used to eliminate the need for trial and error methods in the development stage of single jersey fabrics.