The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.
This study was undertaken to investigate the effect of P/C blend ratio and break draft on yarn quality and physical properties. 15 tex yarn samples were spun from three blend ratios, i.e., P/C 30/70, 50/50, and 70/30 at ring frame with six break drafts (1. 35, 1.38, 1.42, 1.45, 1.49, and 1.53). The results of blend ratio were analyzed through linear regression technique to predict correlation between two variables. Strong correlation observed between blend ratio and yarn quality and physical properties, as the share of polyester increased all yarn characteristics improved. Prominent improvement in yarn mass variation and hairiness was observed when polyester percentage increased from 30% to 50% whereas tenacity and elongation improved more remarkably when polyester share increased to 70% from 50%. Most of the yarn characteristics were found optimum at break draft 1.49.
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