Predicting Yarn Tenacity: A Comparison of Mechanistic, Statistical, and Neural Network Models

Abstract

Prediction of yam properties from fibre properties and process parameters is a weilresearched topic. For a number of years, mechanistic and statisticai models have primarily been used to tackle the problem. Over the last ten years, neural netvforks have been used in increasing numbers for this purpose. However, a comparative assessment of the performance of these three approaches has not been forthcoming. In this paper, all the three models have been applied on the data available to validate the mechanistic model described by Frydrych (pertaining to cotton yarns). The exercise was repeated for data pertaining to yarns spun from polyester staple fibre in the laboratory. The results conclusively prove the superiority of neural networks over mechanistic models and simple regression equations for predicting ring yarn tenacity from fibre properties and process parameters.

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

“…To further ensure a high quality of the input data, the 19 input factors were pre-processed using principal component analysis (PCA) as discussed by Chattopadhyay et al (2001) and Bernstein et al (1988). PCA was designed to come up with a new set of variables that have as little correlation with one another as possible.…”

“…This approach is widely applied in the study of the fiber to yarn process, where yarn properties can be predicted using mathematical, statistical and artificial neural network (ANN) models just to mention a few. A study of the comparison of ANN and other models (mathematical and statistical) has also been undertaken, and reports indicated that the ANN models have comparatively higher prediction efficiency (Guha et al 2001;Ureyen and Gurkan 2008a, b;Majumdar and Majumdar 2004). The efficiency of the ANN algorithms has enabled the design of yarn quality prediction models, which can be used in the spinning industry (Furferi and Gelli 2010).…”

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).…”

Yarn Strength Prediction: A Practical Model Based on Artificial Neural Networks

“…For this reason the problem of yarn properties prediction has been faced by some researchers by employing some knowledge-based approaches like artificial neural networks (ANNs) [7,8] and neuro-fuzzy models [9]. Ramesh et al [10], Zhu and Ethridge [11], Guha et al [12], and Majumdar et al [13] have successfully used the artificial neural network (ANN) and 2 Advances in Mechanical Engineering neuralfuzzy methods to predict various properties of spun yarns. The fabric strength was modeled by Zeydan using neural networks and Taguchi methodologies [14].…”