An Artificial Neural Network-based Hairiness Prediction Model for Worsted Wool Yarns

Khan, Zulfiqar; Lim, Allan Ε. Κ.; Wang, Lijing; Wang, Xungai; Beltran, Rafael

doi:10.1177/0040517508094171

Cited by 37 publications

(24 citation statements)

References 14 publications

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“…Substitution of equation (19) into equation (20) gives the expectation of the number of fiber ends with and above the length of L e in a unit yarn with the length of Á,…”

Section: Twist Correction Twist Correction Of Fiber Lengthmentioning

confidence: 99%

A theoretical model of maximum hairiness of staple ring-spun yarns

Huang

Tao

2014

Textile Research Journal

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This paper proposes a theoretical model for maximum hairiness, defined as the number of fiber ends with a certain length in the surface layer of a unit length of ring-spun yarn. These fiber ends have the potential to become protruding hairs, that is, form hairiness of the spun yarn. With consideration of yarn twist, but excluding loops and wild fibers, this model predicts the maximum yarn hairiness or maximum numbers of potential fiber ends with various lengths, which is derived by employing kernel estimation of fiber length distribution. A hairiness contribution factor is introduced as the ratio between the number of fiber ends in the surface layer that potentially contribute to yarn hairiness and the total number of fiber ends in the yarn cross-section. Previous and the present models associated with hairiness or fiber ends are discussed and compared with the measured number of hairs to verify the proposed theoretical model for maximum hairiness of ring-spun yarn.

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“…Substitution of equation (19) into equation (20) gives the expectation of the number of fiber ends with and above the length of L e in a unit yarn with the length of Á,…”

Section: Twist Correction Twist Correction Of Fiber Lengthmentioning

confidence: 99%

A theoretical model of maximum hairiness of staple ring-spun yarns

Huang

Tao

2014

Textile Research Journal

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“…The overall error estimated for recognizing wool or silk fiber was 5%. Khan et al, (2009) studied the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns objectively by examining 75 sets of yarns consisting of various top specifications and processing parameters of shrink-resist treated, single-ply, pure wool worsted yarns. The results indicated that the MLP model predicted yarn hairiness was more accurately than the MLR model and showed that a degree of nonlinearity existed in the relationship between yarn hairiness and the input factors considered.…”

Section: Review Of Application Of Artificial Neural Network In Textimentioning

confidence: 99%

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

Chiu¹,

Fun²,

Ip³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…Since the network can accurately capture the nonlinear relationships between input and output parameters, they have extremely good predictive power (Behera & Muttagi, 2005). The use of an artificial neural network model as an analytical tool may facilitate material specification/selection and improved processing parameters governed by the predicted outcomes of the model (Khan et al, 2002). An ANN model adjusts itself to establish the relation between the input and the output.…”

Section: Yarn and Fabric Properties Prediction And Modelingmentioning

confidence: 99%