Investigation into the curling behavior of single jersey weft-knitted fabrics and its prediction using neural network model

This research investigates the effect of fiber, yarn, and fabric parameters on curling phenomenon of single jersey weft-knitted fabrics which is interpreted to have curling surface in both course and wale direction. Taguchi’s experimental design is used to estimate the optimum process conditions and to examine the individual effects of all controllable factors on curling one by one. The controllable factors are blending ratio of polyester to cotton fiber, yarn twist and count, fabric structure, knit density, and relaxation time. Results show that fabric structure and knit density have the most dominant effect on the fabric curling. The optimum conditions of minimum curling values were also determined. Finally, the curling surface in course and wale direction as a two features of curling phenomenon was predicted using artificial neural network which selects scale conjugate gradient learning algorithm based on process parameters of single jersey weft-knitted fabrics. Our findings confirm the good capability of artificial neural network algorithm to predict these features.

[1]  K. Choi,et al.  An Energy Model of Plain Knitted Fabric , 2003 .

[2]  V. Subramaniam,et al.  Degree and Stability of the Flat Set of Plain. Weft Knitted Fabrics , 1995 .

[3]  Nuray Ucar.  Prediction of Curling Distance of Dry Relaxed Cotton Plain Knitted Fabrics , 2000 .

[4]  Arif Kurbak,et al.  Basic Studies for Modeling Complex Weft Knitted Fabric Structures Part I: A Geometrical Model for Widthwise Curlings of Plain Knitted Fabrics , 2008 .

[5]  D. L. Munden,et al.  26—THE GEOMETRY AND DIMENSIONAL PROPERTIES OF PLAIN-KNIT FABRICS , 1959 .

[6]  F.T.I. E. M. Walker Esq.,et al.  SOME FUNDAMENTAL PROPERTIES OF HOSIERY YARNS AND THEIR RELATION TO THE MECHANICAL CHARACTERISTICS OF KNITTED FABRICS , 1952 .

[7]  Nuray Ucar.  Prediction of Curling Distance of Dry-Relaxed Cotton Plain Knitted Fabrics , 2001 .

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

[9]  John Stufken,et al.  Taguchi Methods: A Hands-On Approach , 1992 .

[10]  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 .

[11]  M. F. Møller,et al.  Efficient Training of Feed-Forward Neural Networks , 1993 .

[12]  R. Postle,et al.  Bending and Recovery Properties of Wool Plain-Knitted Fabrics , 1974 .

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

[14]  P. Grosberg.  The Mechanical Properties of Woven Fabrics Part II: The Bending of Woven Fabrics , 1966 .

[15]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[16]  M Madhusoothanan,et al.  Modelling of tensile properties of needle-punched nonwovens using artificial neural networks , 2000 .

[17]  Saeed Shaikhzadeh Najar,et al.  A new approach to de‐curling force of single jersey weft‐knitted fabric , 2010 .