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Evaluation of Sewed Thread Consumption of Jean Trousers Using Neural Network and Regression Methods

Research and development

Authors:

  • Jaouachi Boubaker
    Textile Engineering Laboratory, University of Monastir, Monastir, Tunisia
  • Jaouachi Boubaker
    National Engineering School of Monastir, University of Monastir, Monastir, Tunisia
  • Khedher F.
    Textile Engineering Laboratory, University of Monastir, Monastir, Tunisia

Nr DOI: 10.5604/12303666.1152518

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Abstract:

This paper deals with the prediction of the sewing thread consumption of jean trousers using the neural network technique. The neural network results and analysis are discussed and investigated. Indeed the findings show that neural network consumption values give better fitting of experimental results than the ones obtained using regression technique. However, compared to the experimental consumption results, theoretical ones of the sewn jean pants seem widely predictable in the desired field of interest. Among the all parameters studied, statistical analysis results also indicate that five inputs can be considered as influential ones. When classifying these five influential inputs, only three parameters are considered most significant. In fact the thread consumed to sew jean trouser samples remains influenced especially by the thread properties and needle fineness as well. Compared with the regression model, the neural network model gives a more accurate prediction and to a great extent provides the amount of sewing thread.

Tags:

consumption, prediction, sewing, thread, neural network, regression.

Citation:

Jaouachi B, Khedher F. Evaluation of Sewed Thread Consumption of Jean Trousers Using Neural Network and Regression Methods. FIBRES & TEXTILES in Eastern Europe 2015; 23, 3(111): 91-96. DOI: 10.5604/12303666.1152518

Published in issue no 3 (111) / 2015, pages 91–96.

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