2 Artificial Neural Network Prosperities in Textile Applications

Such as other fields, textile industry, deal with numerous large inputs and possible outputs parameters and always feed with a complex interdependence between parameters, it is highly unlikely that an exact mathematical model will ever be developed. Furthermore, since there are many dependent and independent variables during different textile progress, it becomes difficult to conduct and to cover the entire range of the parameters. Moreover, the known and unknown variables cannot be interpolated and extrapolated in a reasonable way based on experimental observations or mill measurements due to the shortage of knowledge on the evaluation of the interaction and significance at weight contributing from each variable. For example, it is quite difficult to develop some universal practical models that can accurately predict yarn quality for different mills (Chattopadhyay & Guha, 2004). Statistical models have also shown up their limitations in use—not least their sensitivity to rogue data—and are rarely used in any branch of the textile industry as a decision-making tool. The mechanistic models proposed by various authors overtly simplify the case to make the equations manageable and pay the price with their limited accuracy. In any case, the vast volume of process parameterrelated data is hardly ever included in these models, making them unsuitable for application in an industrial scenario. By using neural networks, it seems to be possible to identify and classify different textile properties (Guruprasad & Behera, 2010). Some of the studies reported in recent years on the application of neural networks are discussed hereunder.

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[6]  B. Xu,et al.  Clustering Analysis for Cotton Trash Classification , 1999 .

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[12]  Toshio Mori,et al.  Evaluating Wrinkled Fabrics with Image Analysis and Neural Networks , 2002 .

[13]  Y. Li,et al.  Evaluating and Predicting Fabric Bagging with Image Processing , 2002 .

[14]  A. Tilocca,et al.  Detecting Fabric Defects with a Neural Network Using Two Kinds of Optical Patterns , 2002 .

[15]  Hsin-Chung Lien,et al.  A Method of Feature Selection for Textile Yarn Grading Using the Effective Distance Between Clusters , 2002 .

[16]  Tae Jin Kang,et al.  Objective Evaluation of the Trash and Color of Raw Cotton by Image Processing and Neural Network , 2002 .

[17]  C. J. Kuo,et al.  A Back-Propagation Neural Network for Recognizing Fabric Defects , 2003 .

[18]  Chung-Feng Jeffrey Kuo,et al.  Using a Neural Network to Identify Fabric Defects in Dynamic Cloth Inspection , 2003 .

[19]  Ajay Kumar,et al.  Neural network based detection of local textile defects , 2003, Pattern Recognit..

[20]  Moon W. Suh,et al.  Automatic Recognition of Woven Fabric Patterns by an Artificial Neural Network , 2003 .

[21]  K.P.S. Cheng,et al.  Evaluating and Comparing the Physical Properties of Spliced Yarns by Regression and Neural Network Techniques , 2003 .

[22]  A. Jolly-Desodt,et al.  Modeling Color Change after Spinning Process Using Feedforward Neural Networks , 2003 .

[23]  C. J. Kuo,et al.  Using Neural Network Theory to Predict the Properties of Melt Spun Fibers , 2004 .

[24]  Xungai Wang,et al.  Predicting Worsted Spinning Performance with an Artificial Neural Network Model , 2004 .

[25]  Xia Chen,et al.  Evaluating Fabric Pilling with Light-Projected Image Analysis , 2004 .

[26]  P. Majumdar,et al.  Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models , 2004 .

[27]  Magdalena Tokarska,et al.  Neural Model of the Permeability Features of Woven Fabrics , 2004 .

[28]  A. Guha,et al.  ARTIFICIAL NEURAL NETWORKS: APPLICATIONS TO TEXTILES , 2004 .

[29]  Keith C. C. Chan,et al.  Neural Network Prediction of Human Psychological Perceptions of Fabric Hand , 2004 .

[30]  Jiansheng Guo,et al.  Predicting the Warp Breakage Rate in Weaving by Neural Network Techniques , 2005 .

[31]  B. K. Behera,et al.  Comparative Analysis of Modeling Methods for Predicting Woven Fabric Properties , 2005 .

[32]  K. Faez,et al.  The Use of Fundamental Color Stimulus to Improve the Performance of Artificial Neural Network Color Match Prediction Systems , 2005 .

[33]  Xungai Wang,et al.  Predicting the Pilling Propensity of Fabrics through Artificial Neural Network Modeling , 2005 .

[34]  K. Faez,et al.  Use of transformed reflectance functions for neural network color match prediction systems , 2006 .

[35]  E. Shady,et al.  Detection and Classification of Defects in Knitted Fabric Structures , 2006 .

[36]  Jeng-Jong Lin.  Prediction of Yarn Shrinkage using Neural Nets , 2007 .

[37]  C. J. Kuo,et al.  Computerized color separation system for printed fabrics by using backward-propagation neural network , 2007 .

[38]  D. Bhattacharjee,et al.  A Neural Network System for Prediction of Thermal Resistance of Textile Fabrics , 2007 .

[39]  S. Ahadian,et al.  Prediction of Time of Capillary Rise in Porous Media Using Artificial Neural Network (ANN) , 2007 .

[40]  Weidong Yu,et al.  The virtual manufacturing model of the worsted yarn based on artificial neural networks and grey theory , 2007, Appl. Math. Comput..

[41]  M. Shanbeh,et al.  Analysis of Two Modeling Methodologies for Predicting the Tensile Properties of Cotton-covered Nylon Core Yarns , 2007 .

[42]  S. Ahadian,et al.  DETERMINATION OF SURFACE TENSION AND VISCOSITY OF LIQUIDS BY THE AID OF THE CAPILLARY RISE PROCEDURE USING ARTIFICIAL NEURAL NETWORK (ANN) , 2008 .

[43]  Darko Golob,et al.  Determination of Pigment Combinations for Textile Printing Using Artificial Neural Networks , 2008 .

[44]  R. Tuğrul Oğulata,et al.  An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics , 2008 .

[45]  M. Amani Tehran,et al.  The Prediction of Initial Load-extension Behavior of Woven Fabrics Using Artificial Neural Network , 2009 .

[46]  S. Meeran,et al.  Predicting the Seam Strength of Notched Webbings for Parachute Assemblies Using the Taguchi's Design of Experiment and Artificial Neural Networks , 2009 .

[47]  O. Demiryürek,et al.  Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models , 2009 .

[48]  M. Sheikhzadeh,et al.  Moisture and heat transfer in hybrid weft knitted fabric with artificial intelligence , 2009 .

[49]  Wai Keung Wong,et al.  A hybrid model using genetic algorithm and neural network for classifying garment defects , 2009, Expert Syst. Appl..

[50]  Mehmet Dayik,et al.  Prediction of Yarn Properties Using Evaluation Programing , 2009 .

[51]  S. Ramakrishna,et al.  Prediction of water retention capacity of hydrolysed electrospun polyacrylonitrile fibers using statistical model and artificial neural network , 2009 .

[52]  Xungai Wang,et al.  An Artificial Neural Network-based Hairiness Prediction Model for Worsted Wool Yarns , 2009 .

[53]  Ting Chen,et al.  An Input Variable Selection Method for the Artificial Neural Network of Shear Stiffness of Worsted Fabrics , 2009, Stat. Anal. Data Min..

[54]  Bin Gang Xu,et al.  An Artificial Neural Network Model for the Prediction of Spirality of Fully Relaxed Single Jersey Fabrics , 2009 .

[55]  A. Majumdar,et al.  Predicting the properties of needlepunched nonwovens using artificial neural network , 2009 .

[56]  P. Ünal,et al.  The Effect of Fiber Properties on the Characteristics of Spliced Yarns Part I: Prediction of Spliced Yarns Tensile Properties , 2010 .

[57]  U. Nirmal.  Prediction of friction coefficient of treated betelnut fibre reinforced polyester (T-BFRP) composite using artificial neural networks , 2010 .

[58]  B. K. Behera,et al.  Soft computing in textiles , 2010 .

[59]  Dariush Semnani,et al.  Improvement of intelligent methods for evaluating the apparent quality of knitted fabrics , 2010, Eng. Appl. Artif. Intell..

[60]  Philippe Vroman,et al.  Visual Quality Recognition of Nonwovens using Wavelet Texture Analysis and Robust Bayesian Neural Network , 2010 .

[61]  Yu Zhang,et al.  Fabric defect classification using radial basis function network , 2010, Pattern Recognit. Lett..

[62]  Xianyi Zeng,et al.  Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network , 2010, Expert Syst. Appl..