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Research Articles

Artificial neural networks approach for prediction of CIELab values for yarn after dyeing and finishing process

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Pages 1326-1335 | Received 21 Dec 2021, Accepted 21 Aug 2022, Published online: 23 Sep 2022

References

  • Baghdadi, R., Alibi, H., Fayala, F., & Zeng, X. (2016). Investigation on air permeability of finished stretch plain knitted fabrics. I. Predicting air permeability using artificial neural networks. Fibers and Polymers, 17(12), 2105–2115. https://doi.org/10.1007/s12221-016-6800-5
  • Balci, O., & Oğulata, R. (2007). Prediction of the CIELab values of the stripped cotton fabrics using artificial neural network-levenberg marquardt technique. In 6th International Conference-TEXSCI, Czech Republic.
  • Balcı, O., & Oğulata, R. T. (2009). Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models. Fibers and Polymers, 10(3), 384–393. https://doi.org/10.1007/s12221-009-0384-2
  • Balci, O., Oğulata, S. N., Sahin, C., & Oğulata, R. T. (2008). Prediction of CIELab data and wash fastness of nylon 6, 6 using artificial neural network and linear regression model. Fibers and Polymers, 9(2), 217–224. https://doi.org/10.1007/s12221-008-0035-z
  • Chattopadhyay, R., & Guha, A. (2004). Performance of neural networks for predicting yarn properties using principal component analysis. Journal of Applied Polymer Science, 91(3), 1746–1751. https://doi.org/10.1002/app.13231
  • Doran, E. C., & Sahin, C. (2020). The prediction of quality characteristics of cotton/elastane core yarn using artificial neural networks and support vector machines. Textile Research Journal, 90(13–14), 1558–1580. https://doi.org/10.1177/0040517519896761
  • Erenler, A., & Oğulata, R. T. (2015). Investigation and prediction of chosen comfort properties on woven fabrics for clothing. Tekstil ve Konfeksiyon, 25(2), 125–134.
  • Furferi, R., & Carfagni, M. (2010). Prediction of the color and of the color solidity of a jigger-dyed cellulose-based fabric: A cascade neural network approach. Textile Research Journal, 80(16), 1682–1696. https://doi.org/10.1177/0040517510365952
  • Ghaedi, A. M., & Vafaei, A. (2017). Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review. Advances in Colloid and Interface Science, 245, 20–39. https://doi.org/10.1016/j.cis.2017.04.015.
  • Haykin, S. (2009). Neural networks and learning machines. Pearson Education.
  • Hench, K., & Al-Ghanim, A. (1995). The application of a neural network methodology to the analysis of a dyeing operation. In Artificial Neural Networks in Engineering (ANNIE’95). Los Alamos National Lab.
  • Hwang, J. P., Kim, S., & Park, C. K. (2015). Development of a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression. Textile Research Journal, 85(10), 1076–1082. https://doi.org/10.1177/0040517515569525
  • Jawahar, M., Narasimhan Kannan, C. B., & Kondamudi Manobhai, M. (2015). Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system. Coloration Technology, 131(1), 48–57. https://doi.org/10.1111/cote.12123
  • Maghsoudi, M., Ghaedi, M., Zinali, A., Ghaedi, A. M., & Habibi, M. H. (2015). Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study. Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy, 134, 1–9. https://doi.org/10.1016/j.saa.2014.06.106.
  • Moghadam, M., Montazer, M., Nazari, A., Roshani, G., & Ataei, M. (2016). Comparison of RSM and ANN optimization methods in determining antibacterial properties of cotton against E. coli. Journal of Pure and Applied Microbiology, 10(2), 1027–1033.
  • Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & Sons Inc.
  • Pujari, S., Ramakrishna, A., & Padal, K. B. (2017). Comparison of ANN and regression analysis for predicting the water absorption behaviour of jute and banana fiber reinforcedepoxy composites. Materials Today: Proceedings, 4(2), 1626–1633. https://doi.org/10.1016/j.matpr.2017.02.001
  • Ramesh, M. C., Rajamanickam, R., & Jayaraman, S. (1995). The prediction of yarn tensile properties by using artificial neural networks. Journal of the Textile Institute, 86(3), 459–469. https://doi.org/10.1080/00405009508658772
  • Senthilkumar, M. (2007). Modelling of CIELAB values in vinyl sulphone dye application using feed-forward neural networks. Dyes and Pigments, 75(2), 356–361. https://doi.org/10.1016/j.dyepig.2006.06.010
  • Shen, J., Zhou, X., Ma, H., & Chen, W. (2017). Spectrophotometric prediction of pre-colored fiber blends with a hybrid model based on artificial neural network and Stearns–Noechel model. Textile Research Journal, 87(3), 296–304. https://doi.org/10.1177/0040517516629145
  • Thevenet, L., Dupont, D., Jolly., & Desodt, A. (2003). Modeling color change after spinning process using feedforward neural networks. Color Research & Application: Endorsed by Inter‐Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur, 28(1), 50–58.
  • Turhan, Y., & Toprakci, O. (2013). Comparison of high-volume instrument and advanced fiber information systems based on prediction performance of yarn properties using a radial basis function neural network. Textile Research Journal, 83(2), 130–147. https://doi.org/10.1177/0040517512445334
  • Vedaraman, N., Sandhya, K., Charukesh, N., Venkatakrishnan, B., Haribabu, K., Sridharan, M., & Nagarajan, R. (2017). Ultrasonic extraction of natural dye from Rubia cordifolia, optimisation using response surface methodology (RSM) & comparison with artificial neural network (ANN) model and its dyeing properties on different substrates. Chemical Engineering and Processing: Process Intensification, 114, 46–54. https://doi.org/10.1016/j.cep.2017.01.008
  • Westland, S. (1999). Artificial neural networks and colour recipe prediction. In S. M. Burkinshaw (Ed.), Colour science '98: proceedings of the International conference & exhibition (pp. 225–233). Leeds.
  • Yu, C., Xi, Z., Lu, Y., Tao, K., & Yi, Z. (2020). K/S value prediction of cotton fabric using PSO-LSSVM. Textile Research Journal, 90(23–24), 2581–2591. https://doi.org/10.1177/0040517520924750
  • Zhang, J., & Yang, C. (2014). Evaluation model of color difference for dyed fabrics based on the support vector machine. Textile Research Journal, 84(20), 2184–2197. https://doi.org/10.1177/0040517514537372
  • Zhou, Z., Wang, C., Zhang, J., & Zhu, Z. (2020). Color difference classification of solid color printing and dyeing products based on optimization of the extreme learning machine of the improved whale optimization algorithm. Textile Research Journal, 90(2), 135–155. https://doi.org/10.1177/0040517519859933

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