1. bookVolume 18 (2018): Issue 4 (December 2018)
Journal Details
License
Format
Journal
First Published
19 Oct 2012
Publication timeframe
4 times per year
Languages
English
access type Open Access

Predicting Cotton Fibre Maturity by Using Artificial Neural Network

Published Online: 07 Dec 2018
Page range: 429 - 433
Journal Details
License
Format
Journal
First Published
19 Oct 2012
Publication timeframe
4 times per year
Languages
English

Cotton fibre maturity is the measure of cotton’s secondary cell wall thickness. Both immature and over-mature fibres are undesirable in textile industry due to the various problems caused during different manufacturing processes. The determination of cotton fibre maturity is of vital importance and various methods and techniques have been devised to measure or calculate it. Artificial neural networks have the power to model the complex relationships between the input and output variables. Therefore, a model was developed for the prediction of cotton fibre maturity using the fibre characteristics. The results of predictive modelling showed that mean absolute error of 0.0491 was observed between the actual and predicted values, which show a high degree of accuracy for neural network modelling. Moreover, the importance of input variables was also defined.

Keywords

[1] Hu, X.-P. and Y.-L. Hsieh. (1996). Crystalline structure of developing cotton fibers. Journal of Polymer Science Part B: Polymer Physics. 34(8): 1451-1459.Search in Google Scholar

[2] Hsieh, Y.L. 2007. Chemical structure and properties of cotton, in Cotton: Science and technology, Gordon S. and Y.L., Hsieh, Editors. Woodhead Publishing Limited: Cambridge. pp. 3-34.Search in Google Scholar

[3] Basra, A.S., and C.P., Malik. 1984. Development of the cotton fiber, in International review of cytology, G.H. Bourne and J.F. Danielli, Editors., Academic press, Inc.: London.pp. 65-113.Search in Google Scholar

[4] Hsieh, Y.L., X.P. Hu and A. Wang. 2000. Single Fiber Strength Variations of Developing Cotton Fibers-Strength and Structure of G. hirsutum and G. barbedense. Textile Research Journal. 70(8): 682-690.Search in Google Scholar

[5] Matic-Leigh, R., & D.A., Cauthen. (1994). Determining cotton fibre maturity by image analysis part I: Direct measurement of cotton fibre characteristics. Textile Research Journal. 64:534-544.Search in Google Scholar

[6] Warner, S. B. (1995). Maturity of cotton, fibre cross-section and linear density, Fibre Science.Search in Google Scholar

[7] Rieter. (2014). Retrieved from http://www.rieter.com/cz/rikipedia/articles/technology-ofshort-staple-spinning/raw-material-as-a-factor-influencing-spinning/fibre-fineness/fibre-maturity/Search in Google Scholar

[8] Thibodeaux, D.P. & J.P. Evans. (1986). Cotton fiber maturity by image analysis. Textile Research Journal, 56(2):130-139.Search in Google Scholar

[9] Adel, G., F. Faten & A. Radhia. (2011). Assessing cotton fibre maturity and fineness by image analysis. Journal of Engineered Fibres and Fabrics, 6, 50-60.Search in Google Scholar

[10] American Society of Testing Materials. (2012). Standard Test Method for Maturity of Cotton Fibers (Sodium Hydroxide Swelling and Polarized Light Procedures) (D1442-06) ASTM International, West Conshohocken, PA..USA.Search in Google Scholar

[11] Smith, B. (1991). A review of the relationship of cotton maturity and dyeability. Textile Research Journal, 61(3), 137-145.Search in Google Scholar

[12] Paudel, D.R., E.F. Hequet & N. Abidi. (2013). Evaluation of cotton fiber maturity measurements. Industrial crops and products, 45, 435-441.Search in Google Scholar

[13] Abidi, N., E. Hequet, L. Cabrales, J. Gannaway, T. Wilkins, & L.W. Wells. (2008). Evaluating cell wall structure and composition of developing cotton fibers using Fourier transform infrared spectroscopy and thermo-gravimetric analysis. Journal of applied polymer science, 107(1), 476-486.Search in Google Scholar

[14] Wartelle, L.H., J.M. Bradow, O. Hinojosa, A.B. Pepperman, G. Sassenrath-Cole & P. Dastoor. (1995). Quantitative cotton fiber maturity measurements by X-ray fluorescence spectroscopy and advanced fiber information system. Journal of agricultural and food chemistry, 43(5), 1219-1223.Search in Google Scholar

[15] Lord, E. & S.A. Heap. (1988). The origin and assessment of cotton fibre maturity. Int. Institute for Cotton, Technical Research Division, Manchester, England.Search in Google Scholar

[16] Naylor, G.R.S. (2001). Cotton maturity and fineness measurement using the Sirolan-Laserscan.Search in Google Scholar

[17] Montalvo Jr., J.G. (2005). Relationships between micronaire, fineness, and maturity. Part-I. Fundamentals. Journal of Cotton Science, 9, 81-88.Search in Google Scholar

[18] Gordon, S.G. & G.R.S. Naylor. (2004). Instrumentation for rapid direct measurement of cotton fibre fineness and maturity.Search in Google Scholar

[19] Erbil, Y., O. Babaarslan & İ.Ilhan. (2018). A comparative prediction for tensile properties of ternary blended open-end rotor yarns using regression and neural network models. The Journal of The Textile Institute, 109(4): 560-568.”Search in Google Scholar

[20] Kanat, Z.E. & N. Özdil. (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, 1-7.Search in Google Scholar

[21] Mandhyan, P.K., R.P. Nachane, P.G. Patil, B.R. Pawar, H. Hasan & S.S. Venkatkrishnan. (2018). Influence of segregation of cotton bales based on its fiber attributes in yarn properties. Journal of Natural Fibers, 1-9.Search in Google Scholar

[22] Shiau, Y.R., I.S. Tsai & C.S. Lin. (2000). Classifying web defects with a back-propagation neural network by color image processing. Textile Research Journal 70:633-640.Search in Google Scholar

[23] Farooq, A., & C. Cherif. (2008). Use of artificial neural networks for determining the leveling action point at the auto-leveling draw frame.Textile Research Journal, 78, 502-509.Search in Google Scholar

[24] Huang, C.C., & K.T. Chang. (2001). Fuzzy self-organizing and neural network control of sliver linear density in a drawing frame. Textile Research Journal, 71, 987-992.Search in Google Scholar

[25] Cheng, L., & D.L., Adams. (1995). Yarn strength prediction using neural networks Part I: Fibre properties and yarn strength relationship. Textile Research Journal, 65, 495-500.Search in Google Scholar

[26] Sette, S., L., Boullart, L.V., Langenhove,& P., Kiekens. (1997). Optimizing the fiber-to-yarn production process with a combined neural network/genetic algorithm approach. Textile Research Journal, 67, 84-92.Search in Google Scholar

[27] Yang, S. & S. Gordon. (2018). Fiber-to-yarn predictions. In Engineering of High-Performance Textiles (pp. 81-106).Search in Google Scholar

[28] Turhan, Y., & O. Toprakci (2012). 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, 130-147.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo