Applications of Neural Network in Textiles
Department of Textile Engineering
Ahsanullah University of Science & Technology (AUST)
Facebook: Saiful Islam
Department of Textile Engineering
Ahsanullah University of Science & Technology (AUST)
Facebook: Saiful Islam
Neural Network & Artificial Neural Network:
The term Neural Network was traditionally used to refer to a network or circuit of biological neurons. Neural network is a computational structure. In machine learning and computational neuroscience, the term often refers to Artificial Neural Networks (ANN), which is an information processing paradigm that is inspired by the way biological nervous systems. Artificial neural network consists of an interconnected group of artificial neurons and it processes information using a connectionist approach to computation. Neural networks are used for modeling complex relationships between inputs and outputs or to find patterns in data. The ANN has recently been applied in process control, identification, diagnostics, character recognition, sensory prediction, robot vision, and forecasting.
Schematic diagram of biological neurons
Neural Network is an intelligent system that finds widespread applications in many research and engineering fields. It is a computational structure. ANN is a field of computer science that seeks to understand and implement computer based technology that can simulate characteristics of human intelligence includes learning, adapting, reasoning, self-correction and automatic improvement and human sensory capabilities An ANN is a massively parallel distributed processor made up of simple processing units, which has a neural propensity for storing experiential knowledge and making it available for use. It has capability to organize its structural constituents, known as “Neurons”. It can perform certain computations many times faster than the fastest computer.
Applications of Neural Network in Textile:
Artificial neural network is being extensively used at a research stage in all the fields of textile industry since last two decade. Artificial neural network is applied in every field of textile industry such as yarn manufacturing engineering, fabric engineering, dyeing, quality control, lay planning in garment industry, market prediction, fashion prediction identification and classification different textile properties etc. It is one of the hopes because it integrate the elements such as production, quality control, cost, information, statistical process control, just-in-time manufacturing computer integrated manufacturing etc, which make the production smooth & faster. In Textiles and Clothing industries, it involves the interaction of a large number of variables. The relation between 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. The ANN is able to learn such datasets to reveal the unknown relation between various variables effectively. The applications of ANN in textile are huge. Here some applications of ANN in textile are described:
|Artificial neural network|
Using ANN and image processing, an image system, developed by Kang and Kim (2002), for the current cotton grading system of raw cotton involving a trained artificial neural network with a good classifying ability by trash analysis. The effect of trash creates a color difference between a trash-containing image and a trash-removed image. The ANN, has eight color parameters as input data, was a highly reliable and useful tool for classifying color grades automatically and correctly.
Using ANN and image processing, She et al., (2002) developed an intelligent system to classify two kinds of animal fibers objectively between merino and mohair; which are determined in accordance with the complexity of the scale structure and the accuracy of the model.
An artificial neural network developed by Beltran et al., (2004),trained with back-propagation encompassed all known processing variables that existed in different spinning mills, and then generalized this information to accurately predict yarn quality of worsted spinning performance for an individual mill. The ANN was a suitable tool for predicting worsted yarn quality for a specific mill.
By using artificial neural networks (ANN), Farooq and Cherif (2008) have reported a method of predicting the leveling action point, which was one of the important auto-leveling parameters of the drawing frame and strongly influences the quality of the manufactured yarn.
Kuo et al., (2004) applied neural network theory to 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) predicted the tensile properties (yarn tenacity) of air-jet spun yarns produced from 75/25 polyester on an air-jet spin tester by two models, namely neural network model and numerical simulation. A neural network model provided quantitative predictions of yarn tenacity.
By using neural net model Lin (2007) studied the shrinkages of warp and weft yarns of 26 woven fabrics manufactured by air jet loom, which were used to determine the relationships between the shrinkage of yarns and the cover factors of yarns and fabrics.
Xu et al., (2007), analyzed a neural network method of analyzing cross-sectional images of a wool/silk blended yarn. The process of original yarn cross-sectional images including image enhancement and shape filtering; and the determination of characteristic parameters for distinguishing wool and silk fibers in the enhanced yarn cross-sectional images were in the study.
For sliver levelness Huang & Chang, 2001 developed an auto leveling system with a drawing frame using fuzzy self-organizing and neural network applied on a laboratory scale drawing frame with two drafting zones and two sliver doubling samples.
Lien & Lee, 2002 reported feature selection for textile yarn grading to select the properties of minimum standard deviation and maximum recognizable distance between clusters to achieve effectiveness and reduce grading process costs.
Cheng & Lam, 2003 estimated a performance prediction of the spliced cotton yarns by using a regression model and also a neural network model. Different spliced yarn properties such as strength, bending, abrasion, and appearance were merged into a single score which was then used to analyze the overall performance of the yarns by those two models.
Applications to Fabrics:
In Fabric manufacture
Yao et al., (2005) investigated the predictability of the warp breakage rate from a sizing yarn quality index using a feed-forward back-propagation network in an artificial neural network system. An eight quality index (size add-on, abrasion resistance, abrasion resistance irregularity, hairiness beyond 3 mm, breaking strength, breaking strength irregularity, breaking elongation, and breaking elongation irregularity) and warp breakage rates were rated in controlled conditions.
Behera and Karthikeyan (2006) described the method of applying artificial NNs for the prediction of both construction and performance parameters of canopy fabrics. Based on the influence on the performance of the canopy fabric, constructional parameters were chosen.
Applying the artificial neural network, Behera and Goyal (2009) described the method of for the prediction performance parameters for airbag fabrics. The results of the ANN performance prediction had low prediction error of 12% with all the samples and the artificial neural network based on Error Back-propagation were found promising for a new domain of design prediction technique.
In Fabric-property prediction
Ertugrul and Ucar (2000) have shown how the bursting strength of cotton plain knitted fabrics can be predicted before manufacturing by using intelligent techniques of neural network and neuro-fuzzy approaches.
New approaches have taken by Shyr et al., (2004), in using a one-step transformation process to establish translation equations for total hand evaluations of fabrics by employing a stepwise regression method and an artificial neural network.
The prediction of non-linear relations of functional and aesthetic properties of worsted suiting fabrics for fabric development was investigated by Behera and Mishra (2007), by an engineered approach of a radial basis function network which was trained with worsted fabric constructional parameters. Therefore, an objective method of fabric appearance evaluation with the help of digital image processing was introduced.
In Fabric defect
Best wavelet packet bases and an artificial neural network (ANN) are used by Hu and Tsai (2000), to inspect four kinds of fabric defects. Multi-resolution representation of an image using wavelet transform was a new and effective approach for analyzing image information content. The values and positions for the smallest-six entropy were found in a wavelet packet best tree that acted as the feature parameters of the ANN for identifying fabric defects.
By using ANN, Saeidi et al., (2005) described a computer vision-based fabric inspection system implemented on a circular knitting machine to inspect the fabric under construction.
A new method developed by Shady et al., (2006) for knitted fabric defect detection and classification using image analysis and neural networks. Images of six different induced defects (broken needle, fly, hole, barré, thick and thin yarn) were used in the analysis.
A new method for a fabric defect identifying system was developed by Choi et al., (2001) by using fuzzy inference in multi-conditions. The system has applied fuzzy inference rules, and the membership function for these rules to adopt a neural network approach.
Applications to Swing
Jeong et al., (2000) constructed a neural network and subjoined local approximation technique for application to the sewing process by selecting optimal interlinings for woolen fabrics.
Hui et al., (2007) investigated the use of artificial neural networks (ANN) to predict the sewing performance of woven fabrics for efficient planning and control for the sewing operation.
Applications to chemical processing
Image processing and fuzzy neural network approaches are used by Huang and Yu (2001), to classify seven kinds of dyeing defects including filling band in shade, dye and carrier spots, mist, oil stain, tailing, listing, and uneven dyeing on selvage. The fuzzy neural classification system was constructed by a fuzzy expert system with the neural network as a fuzzy inference engine so it was more intelligent in handling pattern recognition and classification problems.
The application of Neural Network (NN) creates new scopes in the field of textile engineering. The results obtained by these intelligent devices are much more precise and reliable than the normal method of measurement / inspection. As mentioned earlier, Textile industries in developed counties have started exploiting these techniques to their advantage.
Most of the textile processes and the related quality assessments are non-linear in nature and hence, neural networks find application in textile technology.
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