Open-End Yarn; Breaking Strength Model
Comparative Approach by Regression and Artificial Neural Network
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Modelling the relationship between key parameters of textile products and machine setting parameters has been recently highlighted by a number of scholars for better prediction of products quality characteristics. Samples were woven for analyzing the characteristics of cotton yarn with different strength, elongation, NEP, thickness, thinness, unevenness, and lint. An Experimental design was conducted by altering three machine parameters of production speed, stretching back, and distances, respectively. The relationship between machine parameters and yarn strength was derived from the Artificia...
Modelling the relationship between key parameters of textile products and machine setting parameters has been recently highlighted by a number of scholars for better prediction of products quality characteristics. Samples were woven for analyzing the characteristics of cotton yarn with different strength, elongation, NEP, thickness, thinness, unevenness, and lint. An Experimental design was conducted by altering three machine parameters of production speed, stretching back, and distances, respectively. The relationship between machine parameters and yarn strength was derived from the Artificial Neural Network model featured with Multiple Layer Propagation (MLP) progressive pattern. The Artificial Neural Network (ANN) showed more reliability and precise to predict various thread properties than the other existing models.
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