Zulfiqar Ali Malik

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.

Comparison of Regression and Adaptive Neuro-fuzzy Models for Predicting the Bursting Strength of Plain Knitted Fabrics

The aim of this study was to compare the response surface regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. The prediction models are based on the experimental data comprising yarn tenacity, knitting stitch length and fabric GSM as input variables and fabric bursting strength as output/response variable. The models quantitatively characterize the non-linear relationship and interactions between the input and output variables exhibiting very good prediction ability and accuracy, with ANFIS model being slightly better in performance than the regression model.

Use of Taguchi Method and Grey Relational Analysis to Optimize Multiple Yarn Characteristics in Open-End Rotor Spinning

Abstract Rotor speed and twist per metres (tpm) are two key parameters in open-end rotor spinning of cotton yarns. High spinning productivity can be obtained by keeping the rotor speed high and twist level as low as possible. However, too high rotor speed may result in yarn imperfections and too low twist level may result in lower tenacity yarns. This study aimed at optimising the multiple yarn characteristics in open-end rotor spinning using the Taguchi method and the grey relational analysis. Cotton yarn samples of 30 tex were produced on rotor spinning machine with different twist levels (i.e. 500, 550, 600 and 700 tpm) at different rotor speeds (i.e. 70,000, 80,000, 90,000 and 100,000 rpm) according to the Taguchi design of experiment. Optimal spinning process parameters were determined using the grey relational grade as the performance index. It was concluded that for the cotton fibres and yarn count used in this study, optimum properties of the yarns could be obtained at 90,000 rpm rotor speed and 700 tpm.

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.

Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics

The aim of this study was to compare the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the wrinkle recovery of polyester/cotton woven fabrics. The prediction models were developed using experimental data-set of 115 fabric samples of different constructions. Warp and weft yarn linear densities, ends/25 mm and picks/25 mm, were used as input/predictor variables, and warp and weft crease recovery angles (CRA) as output/response variables. It was found that the prediction accuracy of the ANN models was slightly better as compared with that of ANFIS models developed in this study. However, the ANFIS models could characterize the relationships between the input and output variables through surface plots, which the ANN models could not. The developed models may be used to optimize the fabric construction parameters for maximizing the wrinkle recovery of polyester/cotton woven fabrics.

Predicting the air permeability of polyester/cotton blended woven fabrics

The aim of this study was to model the air permeability of polyester cotton blended woven fabrics. Fabrics of varying construction parameters i.e. yarn linear densities and thread densities were selected and tested for air permeability, fabric areal density and fabric thickness. A total of 135 different fabric constructions were tested among which 117 were allocated for development of prediction model while the remaining were utilized for its validation. Four variables were selected as input parameters on basis of statistical analysis i.e. warp yarn linear density, weft yarn linear density, ends per 25 mm and picks per 25 mm. Response surface regression was applied on the collected data set in order to develop the prediction model of the selected variables. The model showed satisfactory predictability when applied on unseen data and yielded an absolute average error of 5.1 %. The developed model can be effectively used for prediction of air permeability of the woven fabrics.

Effect of sewing parameters and wash type on the dimensional stability of knitted garments

Abstract The aim of this research is to study the effect of clothing manufacturing parameters, that is, stitch type, stitch density, sewing thread type and washing type on the dimensional stability of single jersey knitted garment. Single jersey bleached fabric, made from Ne 32 cotton combed ring spun yarn, was used to make 32 medium size crew neck T-shirts selecting two levels of stitch type, stitch density, sewing thread type and wash type according to the experimental design. After constructing the garments, four critical measurements of each garment, that is, body length, body width, across shoulder and sleeve length were measured. The constructed garments were divided into two equal groups. One group was washed with water and the other group was washed using a detergent. After washing, drying and tumbling, the same critical measurements of each garment were taken and the percent shrinkage was calculated. Analysis of data was done on responses of output variables against the input variables using MINITAB. The results showed that three input variables: stitch type, stitch density and garment wash type have significant effect on all the output variables.

Response Surface Modeling of Physical and Mechanical Properties of Cotton Slub Yarns

Abstract The objective of this study was to model the physical and mechanical properties of 100% cotton slub yarns commonly used in denim and other casual wear. Statistical models were developed using central composite experimental design of the response surface methodology. Yarn’s linear density, slub thickness, slub length and pause length were used as the key input variables while yarn strength, elongation, coefficient of mass variation, imperfections and hairiness were used as response/output variables. It was concluded that yarn strength and elongation increased with increase in linear density and pause length, and decreased with increase in slub thickness and slub length. Yarn mass variation and total imperfections increased with increase in slub thickness and pause length, whereas yarn imperfections and hairiness decreased with increase in slub length. It was further concluded that due to statistically significant square and interaction effects of some of the input variables, only the quadratic model instead of the linear models can adequately represent the relationship between the input and the output variables. These statistical models will be of great importance for the industrial personnel to improve their productivity and reduce sampling.