Validation of fibre stress utilization model for modified ring spun yarns

To model the fibre stress utilization in modified ring spun yarns, we developed an analytical formula from the experimental data. The development of empirical formulae is carried out by using two different techniques, i.e., Cubic Spline and Artificial Neural Network methods. The experimental data of stress-strain curves of fibre and yarn has a large variation. To cope this variability, we used the smoothing spline technique to find the best-fit curve with respect to a reasonable smoothness. The best nonlinear smooth fitting can be used to extrapolate the experimental data beyond the breaking point. The modified ring spun yarns (compact, SIRO and SIRO-compact) with 20/1, 30/1 and 40/1 English count, produced from viscose staple fibre, were used to predict fibre stress utilization up to the yarn break by extrapolating the mean stress-strain curves of fibre and yarn by using the artificial neural network. Moreover, a new distribution function of fibre distribution in yarn has been proposed and successfully implemented for the prediction of fibre stress utilization in yarn. The new formulation helps to compute the fibre stress utilization in the yarn analytically. The validation of the proposed methodology is presented by comparing the numerical results with the experimental data. The predicted fibre stress utilization was in good agreement with the experimental fibre stress utilization for all types of modified ring spun yarns. It has been observed that SIRO-compact yarn exhibits improved fibre stress utilization as compared to SIRO and compact yarns. Moreover, the new distribution functions Gamma and Gaussian distribution were introduced in parallel with the Dirac delta function. In previous similar studies on ring, rotor and air-jet spun yarns, the proposed model can only predict the fibre stress utilization before the breakage point whereas the modified model, in this study, can predict the fibre stress utilization up to the breaking point.