Assad Farooq

Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame

Artificial neural networks with their ability of learning from data have been successfully applied in the textile industry. The leveling action point is one of the important auto-leveling parameters of the drawing frame and strongly influences the quality of the manufactured yarn. This paper reports a method of predicting the leveling action point using artificial neural networks. Various leveling action point affecting variables were selected as inputs for training the artificial neural networks with the aim to optimize the auto-leveling by limiting the leveling action point search range. The Levenberg—Marquardt algorithm is incorporated into the back-propagation to accelerate the training and Bayesian regularization is applied to improve the generalization of the networks. The results obtained are quite promising.

Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

Abstract 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.

Development of Prediction System Using Artificial Neural Networks for the Optimization of Spinning Process

This article correlates draw frame settings with quality characteristics of sliver and ring spun yarn using artificial neural networks. Considering the importance of draw frame as the last quality improvement machine in the spinning process, the quality influencing parameters of the draw frame were used as input for artificial neural networks. The neural networks were trained using a combination of Levenberg-Marquardt algorithm and Bayesian regularization for better generalization of the networks. Cross validation was performed for each trained network to test the performance of networks. The promising results achieved by this research work emphasize the ability of neural networks to predict the quality characteristics of sliver and yarn using the artificial neural networks. Therefore, draw frame parameters can be adjusted on the basis of required sliver and yarn quality. Furthermore, machines can be involved in the decision making process in spinning mills.

Development of a novel curing system for low temperature curing of resins with the aid of nanotechnology and ultraviolet radiation

Textile processing is an energy intensive process, which contributes about 15–20% of the cost of the finished product. The inefficient equipment and the non-optimized processes are the major causes of the energy losses. The most energy intensive process during wet processing is the curing. Conventionally curing/cross linking of resin is done at a high temperature of about 170 °C. In this research work, ZnO nanoparticles were used in the curing process with the aim to replace the conventional catalyst and to decrease the curing temperature and thermal curing time. The curing of resins was carried out using three different techniques i.e. thermal radiation, UV radiation and a combination of thermal & UV radiation. Promising results have been achieved.

Environment Friendly Finishing for the Development of Oil and Water Repellent Cotton Fabric

ABSTRACT Two environment-friendly short chain fluorocarbons, oleophobol and phobol, were evaluated on cotton fabric. The effect of combination of two fluorocarbons and the combination of oleophobol with zero formaldehyde cross-linker (DHEU) to enhance the performance and durability was also scrutinized. Repellency performance of the treated fabric was appraised by measuring water repellency, oil repellency and contact angle. Washing durability of the finished fabric was also enhanced by the incorporation of the cross-linker (DHEU) into the recipe of oleophobol. The physical and comfort properties of the fabric were also examined by measuring the tensile strength, tear strength and water vapor permeability of the fabric.

Performance Enhancement of Natural Dyes Extracted from Acacia Bark Using Eco-Friendly Cross-Linker for Cotton

ABSTRACT Natural dyes are environment friendly in nature and cannot cause any harmful effects as is the case with most of the synthetic dyes. Nevertheless, most of the natural dyes exhibited weak interaction between fabric and natural dye, which resulted in poor shade depth and durability problems for cotton fabric. This research work is an endeavor to apply natural dyes in combination with eco-friendly cross-linker to enhance the durability and performance of natural dyes. Moreover, pH of the dyeing solution imparted significant effect on color characteristics of fabrics dyed with acacia dye.

Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network

Abstract This research was aimed to develop artificial neural network (ANN) models to predict yarn crimp in woven barrier fabrics. For ANN training, 52 polyester (PES) multifilament barrier fabrics were produced by varying weft yarn and filament fineness, yarn type, weft density, weave type, and loom parameters. The supervised training of neural network was performed using Matlab® ANN toolbox function ‘trainbr’ which is the incorporation of Levenberg-Marquardt (LM) optimization and automated Bayesian regularization into backpropagation. From modeling outcomes, it was observed that both warp and weft yarn crimp models have generalized well with excellent coefficient of determination and trivial mean absolute error when tested on novel data. Moreover, input rank analysis of optimized network provided important information about model stability with respect to input variables, and trend analysis elucidated the input-crimp behavior using different input levels.

Development of a novel method for natural dyeing of cotton fabrics using ultrasonic radiations and acacia bark

ABSTRACT Natural dyeing has been the focus of many previous researches as a substitute for synthetic dyeing, which is a known hazardous process. However, the limitations in the color range and poor fastness properties are the main obstacles in the further development of natural dyeing processes. This work corresponds to the inclusion of ultrasonic energy to enhance the color yield and color fastness properties of fabrics dyed with natural dyes. Cotton fabrics were dyed with acacia dye using conventional infrared exhaust and ultrasonic assisted procedures. Moreover, the effects of different processing factors like temperature, time, and liquor ratio were also investigated, in order to optimize the process. The ultrasonic assisted dyeing shows promising results in terms of both shade depth and washing fastness.

Development of Maghemite Glass Fibre Nanocomposite for Adsorptive Removal of Methylene Blue

Maghemite glass fibre nanocomposite with excellent magnetic and adsorption properties was successfully developed from nontoxic and eco-friendly reagents by thermal decomposition approach. The developed nanocomposite was utilized in adsorption of methylene blue which follows Freundlich adsorption isotherm. The excellent value of adsorption capacity (51.31 mg g-1) as compared to other adsorbents recommends its potential role for adsorption phenomenon in multiple applications. The developed nanocomposite can be recycled and reused easily. Surface and other functional characteristics of developed nanocomposite were determined through scanning electron microscopy, X-ray diffraction, raman spectroscopy, energy dispersive X-ray spectroscopy and vibrating sample magnetometer. The obtained results revealed that maghemite glass nanocomposite is a potential tool that can be utilized in waste water treatments.

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Abstract Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.