Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

• Autorzy: Malik S. A. , Farooq A. , Gereke T. , Cherif C.

Identyfikatory

DOI

10.1515/aut-2015-0018

• Języki publikacji: EN

Abstrakty

EN

The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Słowa kluczowe

EN

blended yarn; evenness; tenacity; model; artificial neural network; multiple linear regression; rank analysis

PL

przędza mieszana; równość; wytrwałość; model; sztuczna sieć neuronowa; regresja wielokrotna; analiza rangowa

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2016
• Tom: Vol. 16, no. 2
• Strony: 43--50
• Opis fizyczny: Bibliogr. 23 poz.

Twórcy

autor

Malik S. A.

• Department of Textile Engineering, Mehran University of Engineering & Technology, 76062 Jamshoro, Sindh, Pakistan
• Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany

autor

Farooq A.

• Department of Fiber and Textile Technology, University of Agriculture Faisalabad, 3800 Faisalabad, Pakistan

autor

Gereke T.

• Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany

autor

Cherif C.

• Institute of Textile Machinery and High Performance Material Technology, Technische Universität Dresden, 01062 Dresden, Germany

Bibliografia

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.