Manufacturing processes in the textile industryexpert Systems for fabrics production

  1. Juan José Bullón Pérez 1
  2. María Angélica González Arrieta 2
  3. María Ascensión Hernández Encinas 3
  4. María Araceli Queiruga Dios 3
  1. 1 Chemical and Textil Engineering Department, University of Salamanca. Spain
  2. 2 Computer Sciences and Control Department, University of Salamanca
  3. 3 Applied Mathematics Department, University of Salamanca
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Year of publication: 2017

Volume: 6

Issue: 4

Pages: 15-23

Type: Article

DOI: 10.14201/ADCAIJ2017641523 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal


Cited by

  • Dimensions Cited by: 21 (09-04-2023)


  • Social Sciences: C

Journal Citation Indicator (JCI)

  • Year 2017
  • Journal Citation Indicator (JCI): 0.07
  • Best Quartile: Q4
  • Area: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Quartile: Q4 Rank in area: 168/170


(Data updated as of 09-04-2023)
  • Total citations: 21
  • Recent citations: 12
  • Field Citation Ratio (FCR): 5.27


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 technicalprocesses lead to the coexistence of different sub-sectors in regards to their business structure and integration. The textile industry is developing expert systems applicationsto 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) have proved 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.

Bibliographic References

  • Bingham, G. A. and Hague, R., 2013. Efficient three dimensional modelling of additive manufactured textiles. Rapid Prototyping Journal, 19(4):269–281.
  • Bullón Pérez, J., González Arrieta, A., Hernández Encinas, A., and Queiruga-Dios, A., 2016. Industrial Cyber-Physical Systems in Textile Engineering. In International Conference on EUropean Transnational Education, pages 126–135. Springer.
  • Burkhard, H. D., 2004. Case completion and similarity in case-based reasoning. Computer Science and Information Systems, 1(2):27–55.
  • Chattopadhyay, R. and Guha, A., 2004. Artificial neural networks: applications to textiles. Textile Progress, 35(1):1–46.
  • Chen, X., 2009. Modelling and predicting textile behaviour. Elsevier.
  • Corchado, J. M. and Lees, B., 2001. A hybrid case-based model for forecasting. Applied Artificial Intelligence, 15(2):105–127.
  • Dwivedi, A. and Dwivedi, A., 2013. Role of Computer and Automation in Design and Manufacturing for Mechanical and Textile Industries: CAD/CAM. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 3(3):8.
  • Eichhoff, J., 2012. Data transmitting Textile Fibre Ropes for Firefighting Applications. Shaker. FAO, 2009. International Year of Natural Fibres 2009. Trade and Markets Division (EST).
  • Fdez-Riverola, F. and Corchado, J., 2000. Sistemas híbridos neuro-simbólicos: Una revisón. Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, 4(11):12–26.
  • Fontana, M., Rizzi, C., and Cugini, U., 2005. 3D virtual apparel design for industrial applications. Computer- Aided Design, 37(6):609–622.
  • Green, T. R., 1989. Cognitive dimensions of notations. People and computers V, pages 443–460. Guruprasad, R., 2010. Soft computing in textiles. Indian Journal of Fibre & Textile Research, 35:75.
  • Kolodner, J., 2014. Case-based reasoning. Morgan Kaufmann.
  • McSherry, D., 2003. Similarity and compromise. In International Conference on Case-Based Reasoning, pages 291–305. Springer.
  • McSherry, D., 2006. Completeness criteria for retrieval in recommender systems. In European Conference on Case-Based Reasoning, pages 9–29. Springer.
  • MODSIMText, P., 2008. Development of a rapid configuration system for textile production machinery based on the physical behaviour simulation of precision textile structures. FP7 EU Research project. Grant Agreement NMP2-SL-2008-214181.
  • Palma Morón, F., 1994. Operaciones fundamentales en la hilatura de fibras textiles. Hespérides.
  • Riesbeck, C. K. and Schank, R. C., 2013. Inside case-based reasoning. Psychology Press.
  • Spencer, D. J., 2001. Knitting technology: a comprehensive handbook and practical guide, volume 16. CRC Press.
  • Villanueva, B. S. and Sánchez-Marré, M., 2012. Case-based reasoning applied to textile industry processes. In International Conference on Case-Based Reasoning, pages 428–442. Springer.