Predicting the Tensile and Air Permeability Properties of Woven FabricsUsing Artificial Neural Network and Linear Regression Models

The objective of this paper is to investigate the predictability of some of woven fabric properties using artificial neural network (ANN) and regression models. For achieving this purpose, a neural network with three layers was adopted. The regression model was of type a multiple – linear regression one. The independent variables were weft yarn count, twist multiplier and weft density; and the dependent ones were tensile strength, breaking extension and air permeability of the woven fabrics. The ANN and regression models were assessed using the Root means square error (RMSE) and the coefficient of determination (R2-value). The findings of this study revealed that ANN is superior to regression model in predicting the woven fabric properties.

[1]  Iwona Frydrych,et al.  A New Approach for Predicting Strength Properties of Yarn , 1992 .

[2]  L. J. Lucas,et al.  Mathematical Fitting of Modulus-Strain Curves of Poly(ethylene Terephthalate) Industrial Yarns , 1983 .

[3]  Mitsuo Matsudaira,et al.  Fabric Handle and Its Basic Mechanical Properties , 2006 .

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

[5]  Aly El-Shiekh,et al.  Tensile Behavior of Twisted Hybrid Fibrous Structures , 1984 .

[6]  A. A. Almetwally,et al.  Core spun yarn and the secret behind its popular appeal , 2011 .

[7]  A. A. Almetwally,et al.  Comparison between physical properties of ring-spun yarn and compact yarns spun from different pneumatic compacting systems , 2015 .

[8]  Cenk Sahin,et al.  The Prediction of Elongation and Recovery of Woven Bi-Stretch Fabric Using Artificial Neural Network and Linear Regression Models , 2006 .

[9]  R. Lippmann,et al.  An introduction to computing with neural nets , 1987, IEEE ASSP Magazine.

[10]  A. A. Almetwally,et al.  Predicting the tensile properties of cotton/spandex core-spun yarns using artificial neural network and linear regression models , 2014 .

[11]  James D. Keeler.  Vision of neural networks and fuzzy logic for prediction and optimization of manufacturing processes , 1992, Defense, Security, and Sensing.

[12]  Lotfi A. Zadeh,et al.  Soft computing and fuzzy logic , 1994, IEEE Software.

[13]  M. C. Ramesh,et al.  The Prediction of Yarn Tensile Properties by Using Artificial Neural Networks , 1995 .

[14]  R. Ogulata.  Air Permeability of Woven Fabrics , 2006 .

[15]  A. A. Almetwally,et al.  Effects of spandex drawing ratio and weave structure on the physical properties of cotton/spandex woven fabrics , 2014 .

[16]  Abhijit Majumdar,et al.  Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns , 2005 .

[17]  J. Fan,et al.  Predicting Garment Drape with a Fuzzy-Neural Network , 2001 .

[18]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[19]  J. W. S. Hearle,et al.  15—The Mechanics of Twisted Yarns: Theoretical Developments , 1961 .

[20]  Michael Belsley,et al.  Yarn Irregularity Parameterisation Using Optical Sensors , 2009 .

[21]  Yehia E. El Mogahzy,et al.  Selecting Cotton Fiber Properties for Fitting Reliable Equations to HVI Data , 1988 .

[22]  Karim Faez,et al.  Fabric handle prediction using neural networks , 1999, NSIP.