Anwaruddin Tanwari

Cold Pad-Batch dyeing method for cotton fabric dyeing with reactive dyes using ultrasonic energy

Reactive dyes are vastly used in dyeing and printing of cotton fibre. These dyes have a distinctive reactive nature due to active groups which form covalent bonds with -OH groups of cotton through substitution and/or addition mechanism. Among many methods used for dyeing cotton with reactive dyes, the Cold Pad Batch (CPB) method is relatively more environment friendly due to high dye fixation and non requirement of thermal energy. The dyed fabric production rate is low due to requirement of at least twelve hours batching time for dye fixation. The proposed CPB method for dyeing cotton involves ultrasonic energy resulting into a one third decrease in batching time. The dyeing of cotton fibre was carried out with CI reactive red 195 and CI reactive black 5 by conventional and ultrasonic (US) method. The study showed that the use of ultrasonic energy not only shortens the batching time but the alkalis concentrations can considerably be reduced. In this case, the colour strength (K/S) and dye fixation (%F) also enhances without any adverse effect on colour fastness of the dyed fabric. The appearance of dyed fibre surface using scanning electron microscope (SEM) showed relative straightening of fibre convolutions and significant swelling of the fibre upon ultrasonic application. The total colour difference values ΔE (CMC) for the proposed method, were found within close proximity to the conventionally dyed sample.

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.

Ultrasonic-assisted dyeing of Nylon-6 nanofibers

We first time report ultrasonic dyeing of the Nylon 6 nanofibers with two disperse dyes CI Disperse blue 56 and CI Disperse Red 167:1 by utilising ultrasonic energy during dyeing process. The Nylon 6 nanofibers were fabricated via electrospinning and dyed via batchwise method with and without sonication. Results revealed that ultrasonic dyeing produce higher color yield (K/S values) and substantially reduces dyeing time from 60min for conventional dyeing to 30min can be attributed to breakage of dye aggregate, transient cavitation near nanofiber surface and mass transfer within/between nanofibers. Color fastness results exhibited good to very good dye fixation. SEM images exhibit insignificant effect of sonication on morphology of the nanofibers. Our research results demonstrate ultrasonic dyeing as a better dyeing technique for Nylon 6 nanofibers with higher color yield and substantially reduced dyeing time.

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.