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Open-End Yarn; Breaking Strength Model
Open-End Yarn; Breaking Strength Model
Knygos.lt klubas Knygos.lt nariams
92,32 €
-30%
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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, resp…
  • Leidėjas:
  • Metai: 2014
  • Puslapiai: 92
  • ISBN-10: 3639660374
  • ISBN-13: 9783639660371
  • Formatas: 15.2 x 22.9 x 0.6 cm, minkšti viršeliai
  • Kalba: Anglų

Open-End Yarn; Breaking Strength Model (el. knyga) (skaityta knyga) | knygos.lt

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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 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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  • Autorius: Amirhossein Mahlouji
  • Leidėjas:
  • Metai: 2014
  • Puslapiai: 92
  • ISBN-10: 3639660374
  • ISBN-13: 9783639660371
  • Formatas: 15.2 x 22.9 x 0.6 cm, minkšti viršeliai
  • Kalba: Anglų

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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