Green technology used in finishing process study of wrinkled cotton fabric by radial basis function (Experimental and modeling analysis)

Hesarian, Mir Saeid; Tavoosi, Jafar

doi:10.22104/aet.2019.3730.1183

Green technology used in finishing process study of wrinkled cotton fabric by radial basis function (Experimental and modeling analysis)

Document Type : Research Paper

Authors

Mir Saeid Hesarian ¹
Jafar Tavoosi ²

¹ Faculty of Textile Engineering, Urmia University of Technology, Urmia, Iran.

² Department of Electrical Engineering, Faculty of Engineering, Ilam University, Ilam, Iran.

10.22104/aet.2019.3730.1183

Abstract

The wrinkling of cotton fabric is an important factor that affects a garment's appearance. This paper evaluated the use of a non-toxic green anti-wrinkle material in the finishing process to address this issue. For this purpose, the chemical structure of the wrinkled cotton sample was evaluated and treated with a scouring and anti-creasing finishing material. Due to the environmental issues created by the toxic material used as finishes, the type of anti-wrinkle material used in this study had the least possible environmental impact. The mechanism of this anti-crease finishing was based on the crosslinking of cellulose molecular chains. This process limited the chain movements made by wrinkling. Accordingly, the effect of the mentioned mechanism and structural parameters such as the thickness, weight, density of the weft yarn, and linear density of the weft yarn (Ne) were evaluated. The wrinkle degree of the samples was analyzed by using a radial basis function neural network (RBFN). This RBFN modeled the relationships between the degree of wrinkling in the fabrics and the mentioned parameters, especially the anti-crease finishing of the samples. The simulation results confirmed the effectiveness of the proposed method.

Keywords

Main Subjects

green technologies for environmental sustainability

References

[1] Hesarian, M. S. (2010). Evaluation of fabric wrinkle by projected profile light line method. The journal of the textile institute, 101(5), 463-470.

[2] Lam, Y. L., Kan, C. W., Yuen, C. W. M. (2011). Wrinkle-resistant finishing of cotton fabric with BTCA-the effect of co-catalyst. Textile research journal, 81(5), 482-493.

[3] Sahin, U. K., Gursoy, N. C., Hauser, P., Smith, B. (2009). Optimization of ionic crosslinking process: an alternative to conventional durable press finishing. Textile research journal, 79(8), 744-752.

[4] Lacasse, K., Baumann, W. (2004). Textile chemicals: Environmental data and facts. Berlin, Springer, 24, 1015–1036.

[5] Lineken, E. E., Davis, S. M., Jorgensen, C. M. (1956). Cellulose Interactions with certain textile resins. Textile research journal, 26(12), 940-947.

[6] Stam, P. B., Poon, G. S., Spangler, M. J., Underwood, M. P. (1956). Minimum care cotton fabrics. Textile research journal, 26(12), 960-965.

[7] Berbner, H. (1990). Eine neue Faser für Schutzkleidung gegen Hitze und Feuer auf Basis Melaminharz. Chemiefasern/Textilindustrie, 40(92), 154-157.

[8] Widler, G., Eichhorn, U. (1997). Dyeing and printing of Basofil fabrics. Technische textilien, 40, E26-E26.

[9] Cooke, T. F., Roth, P. B., Salsbury, J. M., Switlyk, G., Van Loo, W. J. (1957). Comparison of wrinkle-resistant finishes for cotton. Textile research journal, 27(2), 150-165.

[10] Boosaeidia, N., Pourkhabbaz, A., Jahanib, M. (2017). Biosorption of hexavalent Chromium by the agricultural wastes of the cotton and barberry plants. Advances in environmental technology, 3(3), 159-167.

[11] Askari, N., Farhadian, M., Razmjou, A. (2015). Decolorization of ionic dyes from synthesized textile wastewater by nanofiltration using response surface methodology. Advances in environmental technology, 2, 85-92

[12] Lai, K. H., Wong, C. W. (2012). Green logistics management and performance: Some empirical evidence from Chinese manufacturing exporters. Omega, 40(3), 267-282.

[13] Xuezhong, C., Linlin, J., Chengbo, W. (2011). Business process analysis and implementation strategies of greening logistics in appliances retail industry. Energy procedia, 5, 332-336.

[14] Asad, Y. P., Shamsi, A., Tavoosi, J. (2017). Backstepping-based recurrent type-2 fuzzy sliding mode control for MIMO systems (MEMS triaxial gyroscope case study). International journal of uncertainty, fuzziness and knowledge-based systems, 25(02), 213-233.

[15] Tavoosi, J., Badamchizadeh, M. A., Ghaemi, S. (2011). Adaptive inverse control of nonlinear dynamical system using type-2 fuzzy neural networks. Journal of control, 5(2), 52-60.

[16] Tavoosi, J., Rahmati, S. (2016). New applications on linguistic mathematical structures and stability analysis of linguistic fuzzy models. International Journal of Smart Electrical Engineering, 5(3), 153-159.

[17] Vu, C. C., Kim, J. (2018). Human motion recognition using SWCNT textile sensor and fuzzy inference system based smart wearable. Sensors and actuators A: Physical, 283, 263-272

[18] Colasante, G., Gosling, P. D. (2016). Including shear in a neural network constitutive model for architectural textiles. Procedia engineering, 155, 103-112.

[19] Valente, G. F. S., Mendonça, R. C. S., Pereira, J. A. M., Felix, L. B. (2014). Artificial neural network prediction of chemical oxygen demand in dairy industry effluent treated by electrocoagulation. Separation and purification technology, 132, 627-633.

[20] Karayiannis, N. B. (1997, June). Gradient descent learning of radial basis neural networks. In proceedings of international conference on neural networks (ICNN'97) (Vol. 3, pp. 1815-1820). IEEE.

[21] Suzuki, K. (2011). Artificial Neural Networks - Industrial and Control Engineering Applications, InTech.

[22] Chauhan, N., Yadav, N., Arya, N. (2018). Applications for artificial neural network in textiles. International Journal current microbiol applied sciences, 7, 3134-3143.

[23] Kanat, Z. E., Özdil, N. (2018). Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content. The journal of the textile institute, 109(9), 1247-1253.