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Research Articles

Development of shade prediction system to quantify the shade change after crease recovery finish application using artificial neural networks

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Pages 1287-1294 | Received 04 Feb 2020, Accepted 17 Aug 2020, Published online: 14 Sep 2020

References

  • Bahlmann, C., Heidemann, G., & Ritter, H. (1999). Artificial neural networks for automated quality control of textile seams. Pattern Recognition, 32(6), 1049–1060. https://doi.org/10.1016/S0031-3203(98)00128-9
  • 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
  • Bedaux, J., & Van Leeuwen, W. (2004). Biologically inspired learning in a layered neural net. Physica A: Statistical Mechanics and Its Applications, 335(1-2), 279–299. https://doi.org/10.1016/j.physa.2003.12.008
  • Bhattacharjee, D., & Kothari, V. K. (2007). A neural network system for prediction of thermal resistance of textile fabrics. Textile Research Journal, 77(1), 4–12. https://doi.org/10.1177/0040517506070065
  • Çelik, N., İçoğlu, H. İ., & Erdal, P. (2011). Effect of the particle size of fluorocarbon‐based finishing agents on fastness and color properties of 100% cotton knitted fabrics. Journal of the Textile Institute, 102(6), 483–490. https://doi.org/10.1080/00405000.2010.489743
  • Chattopadhyay, R., & Guha, A. (2004). Artificial neural networks: Applications to textiles. Textile Progress, 35(1), 1–46. https://doi.org/10.1080/00405160408688961
  • Chen, C.-S., Su, S.-L. (2010). Resilient back-propagation neural network for approximation 2-D GDOP. Paper Presented at the Proceedings of the International Technical Multi Conference of Engineers and Computer Scientists, Chengdu, China.
  • Çil, M., Nergis, U., & Candan, C. (2009). An experimental study of some comfort-related properties of cotton—acrylic knitted fabrics. Textile Research Journal, 79(10), 917–923. https://doi.org/10.1177/0040517508099919
  • Daneshvar, N., Khataee, A., & Djafarzadeh, N. (2006). The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing CI Basic Yellow 28 by electrocoagulation process. Journal of Hazardous Materials, 137(3), 1788–1795. https://doi.org/10.1016/j.jhazmat.2006.05.042
  • Daniel, G. (2013). Principles of artificial neural networks (Vol. 7). World Scientific.
  • Değirmenci, Z., & Topalbekiroğlu, M. (2010). Effects of weight, dyeing and the twist direction on the spirality of single jersey fabrics. Fibres & Textiles in Eastern Europe, 18, 81–85.
  • Dutta, M., Chatterjee, A., & Rakshit, A. (2006). Intelligent phase correction in automatic digital ac bridges by resilient backpropagation neural network. Measurement, 39(10), 884–891. https://doi.org/10.1016/j.measurement.2006.07.001
  • Farooq, A., & Cherif, C. (2008). Use of artificial neural networks for determining the leveling action point at the auto-leveling draw frame. Textile Research Journal, 78(6), 502–509. https://doi.org/10.1177/0040517507087677
  • Farooq, A., & Cherif, C. (2012). Development of prediction system using artificial neural networks for the optimization of spinning process. Fibers and Polymers, 13(2), 253–257. https://doi.org/10.1007/s12221-012-0253-2
  • Fernando, T., Maier, H., Dandy, G., May, R. ( (2005). ). Efficient selection of inputs for artificial neural network models. Paper presented at the Proc. of MODSIM 2005 International Congress on Modelling and Simulation: Modelling and Simulation Society of Australia and New Zealand.
  • Gangakhedkar, N. (2010). Colour measurement methods for textiles. In Colour measurement (pp. 221–252). Elsevier.
  • Gay, J., Hirschler, R. (2002). Industrial color tolerance limits: case studies in the textile industry. Paper presented at the 9th Congress of the International Colour Association.
  • Gong, R., & Chen, Y. (1999). Predicting the performance of fabrics in garment manufacturing with artificial neural networks. Textile Research Journal, 69(7), 477–482. https://doi.org/10.1177/004051759906900703
  • Gonzalez Viejo, C., Torrico, D. D., Dunshea, F. R., & Fuentes, S. (2019). Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system. Beverages, 5(2), 33. https://doi.org/10.3390/beverages5020033
  • Grajeck, E., & Petersen, W. (1962). Oil and water repellent fluorochemical finishes for cotton. Textile Research Journal, 32(4), 320–331. https://doi.org/10.1177/004051756203200408
  • Hassoun, M. H. (1995). Fundamentals of artificial neural networks. MIT Press.
  • Hussain, T., Safdar, F., Nazir, A., & Iqbal, K. (2013). Optimizing the shrinkage and bursting strength of knitted fabrics after resin finishing. Journal of Chemical Society Pakistan, 35(6), 1452–1456.
  • Islam, A., Akhter, S., & Mursalin, T. E. (2006). Automated textile defect recognition system using computer vision and artificial neural networks. Statistics, 50, 100–105.
  • Lin, C. R., Xu, J. F., & Xu, J. L. (2012). Prediction Algorithm of Spectral Reflectance of Spot Color Ink Based on Color Parallel and Superposition Model. Advanced Materials Research, 430–432, 1176–1182. https://doi.org/10.4028/www.scientific.net/AMR.430-432.1176
  • Mikučionienė, D., & Laureckienė, G. (2009). The influence of drying conditions on dimensional stability of cotton weft knitted fabrics. Materials Science, 15(1), 64–68.
  • Moghassem, A., & Tayebi, H. (2009). The effect of mercerization treatment on dimensional. properties of cotton plain weft knitted fabric. World Applied Science Journal, 7(10), 1317–1325.
  • Mukthy, A. A., & Azim, A. Y. M. A. (2014). Effects of resin finish on cotton blended woven fabrics. International Journal of Scientific Engineering and Technology, 990(3), 983–990.
  • Naoum, R. S., Abid, N. A., & Al-Sultani, Z. N. (2012). An enhanced resilient backpropagation artificial neural network for intrusion detection system. International Journal of Computer Science and Network Security (IJCSNS), 12(3), 11.
  • Nasouri, K., Bahrambeygi, H., Rabbi, A., Shoushtari, A. M., & Kaflou, A. (2012). Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks. Journal of Applied Polymer Science, 126(1), 127–135. https://doi.org/10.1002/app.36726
  • Pani, A. K., & Mohanta, H. K. (2015). Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. ISA Transactions, 56, 206–221. https://doi.org/10.1016/j.isatra.2014.11.011
  • Quaynor, L., Takahashi, M., & Nakajima, M. (2000). Effects of laundering on the surface properties and dimensional stability of plain knitted fabrics. Textile Research Journal, 70(1), 28–35. https://doi.org/10.1177/004051750007000105
  • Saini, L. M. (2008). Peak load forecasting using Bayesian regularization, Resilient and adaptive backpropagation learning based artificial neural networks. Electric Power Systems Research, 78(7), 1302–1310. https://doi.org/10.1016/j.epsr.2007.11.003
  • Schindler, W. D., & Hauser, P. J. (2004). Chemical finishing of textiles. Elsevier.
  • Xie, C., Liu, J., Zhang, X., Xie, W., Sun, J., Chang, K., Kuo, J., Xie, W., Liu, C., Sun, S., Buyukada, M., & Evrendilek, F. (2018). Co-combustion thermal conversion characteristics of textile dyeing sludge and pomelo peel using TGA and artificial neural networks. Applied Energy, 212, 786–795. https://doi.org/10.1016/j.apenergy.2017.12.084
  • Yadav, V., & Kothari, V. (2004). Prediction of air-jet textured yarn properties using statistical method and neural network. Indian Journal of Fibre and Textile Research, 29(2), 149-156.
  • Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423–1447.
  • Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
  • Zhu, R., & Ethridge, M. (1996). The prediction of cotton yarn irregularity based on the ‘AFIS’measurement. Journal of the Textile Institute, 87(3), 509–512. https://doi.org/10.1080/00405009608631352

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