Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades [chapter]

Chi Leung Parick Hui, Ng Sau, Connie Ip
2011 Artificial Neural Networks - Industrial and Control Engineering Applications  
Introduction An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application,
more » ... fic application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting. In Textiles and Clothing industries, it involves the interaction of a large number of variables. Because of the high degree of variability in raw materials, multistage processing and a lack of precise control on process parameters, the relation between such variables and the product properties is relied on the human knowledge but it is not possible for human being to remember all the details of the process-related data over the years. As the computing power has substantially improved over last decade, the ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. Therefore, the application of ANN is more widespread in textiles and clothing industries over last decade. In this chapter, it aims to review current application of ANN in textiles and clothing industries over last decade. Based on literature reviews, the challenges encountered by ANN used in the industries will be discussed and the potential future application of ANN in the industries will also be addressed. The structure of this chapter comprises of seven sections. The first section includes background of ANN, importance of ANN in textiles and clothing and the arrangement of this chapter. In forthcoming three sections, they include review of applications of ANN in fibres and yarns, in chemical processing, and in clothing over last decade. Afterwards, challenges encountered by ANN used in textiles and clothing industries will be discussed and potential future application of ANN in textiles and clothing industries will be addressed in last section. Artificial Neural Networks -Industrial and Control Engineering Applications 4 2. Applications to fibres and yarns 2.1 Fibre classification Kang and Kim (2002) developed an image system for the current cotton grading system of raw cotton involving a trained artificial neural network with a good classifying ability. Trash from a raw cotton image can be characterized by a captured color by a color CCD camera and acquire color parameters. The number of trash particles and their content, size, size distribution, and spatial density can be evaluated after raw cotton images of the physical standards are thresholded and connectivity was checked. The color grading of raw cotton can be influenced by trash. Therefore, the effect of trash on color grading was investigated using a color difference equation that measured the color difference between a trash-containing image and a trash-removed image. The artificial neural network, which has eight color parameters as input data, was a highly reliable and useful tool for classifying color grades automatically and objectively. She et al., (2002) developed an intelligent system using artificial neural networks (ANN) and image processing to classify two kinds of animal fibres objectively between merino and mohair; which are determined in accordance with the complexity of the scale structure and the accuracy of the model. An unsupervised artificial neural network was used to extract eighty, fifty, and twenty implicit features automatically while image processing technique was used to extract nine explicit features. Then the supervised ANN was employed to classify these fibers, based on the features extracted with image processing and unsupervised artificial neural networks. The classification with features extracted explicitly by image processing is more accurate than with features from unsupervised artificial neural networks but it required more effort for image processing and more prior knowledge. On the contrary, the classification with combined unsupervised and supervised ANN was more robust because it needed only raw images, limited image processing and prior knowledge. Since only ordinary optical images taken with a microscope were employed, this approach for many textile applications without expensive equipment such as scanning electron microscopy can be developed. Durand et al., (2007) studied different approaches for variable selection in the context of near-infrared (NIR) multivariate calibration of the cotton-viscose textiles composition. First, a model-based regression method was proposed. It consisted of genetic algorithm optimization combined with partial least squares regression (GA-PLS). The second approach was a relevance measure of spectral variables based on mutual information (MI), which can be performed independently of any given regression model. As MI made no assumption on the relationship between X and Y, non-linear methods such as feed-forward artificial neural network (ANN) were thus encouraged for modeling in a prediction context (MI-ANN). GA-PLS and MI-ANN models were developed for NIR quantitative prediction of cotton content in cotton-viscose textile samples. The results were compared to full spectrum (480 variables) PLS model (FS-PLS). The model required 11 latent variables and yielded a 3.74% RMS prediction error in the range 0-100%. GA-PLS provided more robust model based on 120 variables and slightly enhanced prediction performance (3.44% RMS error). Considering MI variable selection procedure, great improvement can be obtained as 12 variables only were retained. On the basis of these variables, a 12 inputs of ANN model was trained and the corresponding prediction error was 3.43% RMS error. Beltran et al., (2004) developed an artificial neural network (ANN) trained with back-propagation encompassed all known processing variables that existed in different Kuo et al., (2004) applied neural network theory to consider the extruder screw speed, gear pump gear speed, and winder winding speed of a melt spinning system as the inputs and the tensile strength and yarn count of spun fibers as the outputs. The data from the experiments were used as learning information for the neural network to establish a reliable prediction model that can be applied to new projects. The neural network model can predict the tensile strength and yarn count of spun fibers so that it can provide a very good and reliable reference for spun fiber processing. Zeng et al., (2004) tried to predict the tensile properties (yarn tenacity) of air-jet spun yarns produced from 75/25 polyester on an air-jet spintester by two models, namely neural network model and numerical simulation. Fifty tests were undergone to obtain average yarn tenacity values for each sample. A neural network model provided quantitative predictions of yarn tenacity by using the following parameters as inputs: first and second nozzle pressures, spinning speed, distance between front roller nip and first nozzle inlet, and the position of the jet orifice in the first nozzle so that the effects of parameters on yarn tenacity can be determined. Meanwhile, a numerical simulation provided a useful insight into the flow characteristics and wrapping formation process of edge fibers in the nozzle of an air-jet spinning machine; hence, the effects of nozzle parameters on yarn tensile properties can be predicted. The result showed that excellent agreement was obtained between these two methods. Moreover, the predicted and experimental values agreed well to indicate that the neural network was an excellent method for predictors. Lin (2007) studied the shrinkages of warp and weft yarns of 26 woven fabrics manufactured by air jet loom by using neural net model which were used to determine the relationships between the shrinkage of yarns and the cover factors of yarns and fabrics. The shrinkages were affected by various factors such as loom setting, fabric type, and the properties of warp and weft yarns. The neural net was trained with 13 experimental data points. A test on 13 data points showed that the mean errors between the known output values and the output values calculated using the neural net were only 0.0090 and 0.0059 for the shrinkage ratio of warp (S1) and weft (S2) yarn, respectively. There was a close match between the actual and predicted shrinkage of the warp (weft) yarn. The test results gave R 2 values of 0.85 and 0.87 for the shrinkage of the warp (i.e., S1) and weft (i.e., S2), respectively. This showed that the Yarn manufacture Yarn-property prediction
doi:10.5772/16003 fatcat:b6mssqmpojajvjjkk7uxg47ssm