Performance of neural network algorithms during the prediction of yarn breaking elongation

Abstract

Yarn breaking elongation is one of the most important yarn quality characteristics, since it affects the manufacture and usability of woven and knitted fabrics. One of the methods used to predict the breaking elongation of ring spun yarn is artificial neural network (NN). The design of an NN involves the choice of several parameters which include the network architecture, number of hidden layers, number of neurons in the hidden layers, training, learning and transfer functions. This paper endeavors to study the performance of NN as the design factors are varied during the prediction of cotton ring spun yarn breaking elongation. A study of the relative importance of the input parameters was also undertaken. The results indicated that there is a significant difference in the types of transfer and training functions used. Of the two transfer functions used, purelin performed far much better than logsig function. Among the five training functions, the best training functions in terms of performance was Levenberg-Marquardt. The study of the relative importance of input factors revealed that yarn twist, yarn count, fiber elongation, length, length uniformity and spindle speed, were the six most influential factors.

The use of extreme learning machines (ELM) algorithms to prediction strength for cotton ring spun yarn

“…The aim of this research work was to study the prediction of yarn strength. While the selection of input factors was based on published works (Mwasiagi et al 2008(Mwasiagi et al , 2012) (see Table 1), data pre-processing was also undertaken to ensure better performance of the models. The cotton lint and yarn samples were collected from Kenyan factories, and the cotton and yarn samples tested according to testing standards, in fiber and yarn laboratory.…”

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

“…The prediction of the tensile properties of yarns is of main interests of the international research community. Many publications appeared dealing with the prediction of the tensile properties of the yarns under general (Ramesh et al, 1995;Guha et al, 2001;Nurwaha & Wang, 2008;Mwasiagi et al, 2008;Nurwaha & Wang, 2010) or under specific conditions, such as is the case of core spun yarns (Gharehaghaji et al, 2007), air-jet spun yarns (Zeng et al, 2004) or for the estimation of the torque of worsted yarns (Tran & Phillips, 2007). The ANN prediction method is compared with the Support Vector Machine (SVM) approach and conditions under which each method is better suited are investigated (Ghosh & Chatterjee, 2010).…”

Mechanical-Property-Based Comparison of Paper Yarn with Cotton, Viscose, and Polyester Yarns

“…Other important characteristics to investigate for such yarn include its washability, shrinkage, pilling, and tensile strength. The tensile strength and yarn structure are two of the most important parameters to test for a knitted material (Das 2010;Mwasiagi, Huang, and Wang 2008). Pilling, an undesirable effect caused by fabric abrasion, often yields uneven material surfaces.…”