Mill specific prediction of worsted yarn performance
Data(s) |
01/01/2006
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Resumo |
Different spinning mills use different raw materials, processing methodologies, and equipment, all of which influence the quality of the yarns produced. Because of many variables, there is a difficulty in developing a universal empirical/theoretical model. This work presents a multilayer perceptron algorithm (MLP) model for the purpose of building a mill specific worsted spinning performance prediction tool. Sixteen inputs are used to predict key yarn properties and spinning performance, including number of fibers in cross-section, unevenness (U%), thin places, neps, yarn tenacity, elongation at break, thick places, and spinning ends-down. Validation of the model on mill specific commercial data set shows that the general fit to the target values is good. Importantly, the performance of the MLP shows a certain degree of stability to different, random selections of independent test data. Subsequent comparison against the predicted outputs of Sirolan Yarnspec™ confirms the overall performance of the artificial neural network (ANN) method to be more accuratefor mill specific predictions. <br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Taylor & Francis |
Relação |
http://dro.deakin.edu.au/eserv/DU:30003645/wang-millspecificprediction-2006.pdf http://www.informaworld.com/openurl?genre=article&issn=0040-5000&volume=97&issue=1&spage=11 |
Direitos |
2006, Taylor & Francis |
Palavras-Chave | #artificial neural network #mill specific prediction #worsted spinning performance #yarn quality |
Tipo |
Journal Article |