Manufacturing processes in the textile industryexpert Systems for fabrics production

  1. BULLÓN PÉREZ, J. 1
  2. GONZÁLEZ ARRIETA, A. 2
  3. HERNÁNDEZ ENCINAS, A. 3
  4. QUEIRUGA-DIOS, A. 3
  1. 1 Chemical and Textil Engineering Department, University of Salamanca. Spain
  2. 2 Computer Sciences and Automation Department. University of Salamanca
  3. 3 Applied Mathematics Department. University of Salamanca
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Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Año de publicación: 2017

Volumen: 6

Número: 1

Páginas: 41-50

Tipo: Artículo

DOI: 10.14201/ADCAIJ2017614150 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Resumen

The textile industry is characterized by the economic activity whose objective is the production of fibres, yarns, fabrics, clothing and textile goods for home and decoration,as well as technical and industrial purposes. Within manufacturing, the Textile is one of the oldest and most complex sectors which includes a large number of sub-sectors covering the entire production cycle, from raw materials and intermediate products, to the production of final products. Textile industry activities present different subdivisions, each with its own traits. The length of the textile process and the variety of its technical processes lead to the coexistence of different sub-sectors in regards to their business structure and integration. The textile industry is developing expert systems applications to increase production, improve quality and reduce costs. The analysis of textile designs or structures includes the use of mathematical models to simulate the behavior of the textile structures (yarns, fabrics and knitting). The Finite Element Method (FEM) has largely facilitated the prediction of the behavior of that textile structure under mechanical loads. For classification problems Artificial Neural Networks (ANNs) haveproved to be a very effective tool as a quick and accurate solution. The Case-Based Reasoning (CBR) method proposed in this study complements the results of the finite element simulation, mathematical modeling and neural networks methods.

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