Predicting tensile strength of yarns required for producing PET/Cotton blended woven fabrics of a pre-defined tensile strength

This study was aimed at developing statistical models for the prediction of tensile strength of warp and weft yarns required for attaining a pre-defined strength of PET/Cotton blended woven fabrics. The models were developed based on the empirical data obtained from carefully developed 234 fabric samples with different constructions using 15, 20, and 25 tex yarns in warp and weft directions. The prediction ability and accuracy of the developed models were assessed by correlation analyses of the predicted and actual warp and weft yarn strength values of another set of 36 fabric samples. The analyses showed a very strong ability and accuracy of the developed statistical prediction models.     

Selection of yarn for the predefined tensile strength of cotton woven fabrics

In order to meet the required strength of a fabric, selection of yarn is difficult because tensile strength of woven fabric depends upon a number of factors. Still, the manufacturers have to use hit and trial method in order to select the yarn for the required tensile strength of fabric. This study was carried out to develop regression equations for the prediction of yarn tensile strength suitable for the predefined strength of cotton woven fabrics. These equations were developed by using empirical data obtained from two hundred and thirty four fabric samples prepared under a systematic plan with different constructions. Prediction proficiency and precision of these regression equations were evaluated by correlation analysis of the predicted and actual warp and weft yarn strength values of another set of thirty six fabric samples. The results show a very strong prediction precision of the equations.     

Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks

Tensile strength plays a vital role in determining the mechanical behavior of woven fabrics. In this study, two artificial neural networks have been designed to predict the warp and weft wise tensile strength of polyester cotton blended fabrics. Various process and material related parameters have been considered for selection of vital few input parameters that significantly affect fabric tensile strength. A total of 270 fabric samples are woven with varying constructions. Application of nonlinear modeling technique and appreciable volume of data sets for training, testing and validating both prediction models resulted in best fitting of data and minimization of prediction error. Sensitivity analysis has been carried out for both models to determine the contribution percentage of input parameters and evaluating the most impacting variable on fabric strength.     

Multiple response optimization of rotor yarn for strength,unevenness, hairiness and imperfections

In this study, a multiple response optimization model based on response surface methodology was developed to determine the best rotor speed and yarn twist level for optimum rotor yarn strength and unevenness, and minimum yarn hairiness and imperfections. Cotton yarn of 30 tex, was produced on rotor spinning machine with different twist levels (i.e. 500, 550, 600 and 700 tpm) at different rotor speeds (i.e. 70000, 80000, 90000 and 100000 rpm). Yarn quality characteristics were determined for all the experiments. Based on the results, multiple response optimization model was developed using response surface regression on MINITAB® 16 statistical tool. Optimization results indicate that with the quality of raw material selected for this study, top 50 % quality level, according to USTER® yarn quality benchmarks, can be achieved with 100 % desirability satisfaction for all the selected yarn quality parameters at rotor speed of 77,800 rpm and yarn twist of 700 twists per meter.