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Nurwaha D.,Key Laboratory of Science and Technology of Eco Textile | Wang X.,Donghua University
Fibers and Polymers | Year: 2011

A new method for rotor spun yarn prediction from fiber properties based on the theory of support vector machines (SVM) was introduced. The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this study, high volume instrument (HVI) and advanced fiber information system (Uster AFIS) fiber test results consisting of different fiber properties are used to predict the rotor spun yarn strength. The results obtained through this study indicated that the SVM method would become a powerful tool for predicting rotor spun yarn strength. The relative importance of each fiber property on the rotor spun yarn strength is also expected. The study shows also that the combination of SVM parameters and optimal search method chosen in the model development played an important role in better performance of the model. The predictive performances are estimated and compared to those provided by ANFIS model. © 2011 The Korean Fiber Society and Springer Netherlands. Source

Sun Y.,Donghua University | Wang X.,Donghua University | Wang X.,Key Laboratory of Science and Technology of Eco Textile
Journal of the Textile Institute | Year: 2011

In this paper, a method combining the orthogonal array design and the numerical simulation is proposed to optimize the geometry parameters of the melt-blowing slot die. An index, the stagnation temperature, is used to evaluate the performance of the slot die. The stagnation temperature is obtained by simulating the subsonic compressible air jet from the melt-blowing slot die, whereas the optimization is accomplished by the orthogonal array method. Three geometry parameters of the slot die: slot width, nose piece width, and slot angle are investigated. The results show that smaller slot angle and larger slot width will result in a higher stagnation temperature, which is beneficial to the air drawing of the polymer melt and thus to reducing fiber diameter, whereas the effect of nose piece width is insignificant. The optimal geometry parameters of the melt-blowing slot die achieved in this study are: slot width of 1.5 mm, slot angle of 30°, and nose piece width of 2 mm. © 2011 The Textile Institute. Source

Han W.,Donghua University | Nurwaha D.,Donghua University | Li C.,Donghua University | Wang X.,Donghua University | Wang X.,Key Laboratory of Science and Technology of Eco Textile
Polymer Engineering and Science | Year: 2014

In this study, a free surface electrospinning experimental setup was developed based on rotating spiral copper wire electrode and used as the spinneret. The scheme was investigated by varying processing parameters including polymer solution concentration, distance between the electrode and the collector, applied voltage between the electrode, and the collector and wire electrode diameter. An average of fiber diameter ranged between 202 and 543 nm and a relative standard deviation ranged between 11.0 and 26.9% were obtained. The combined effects of processing parameters on the resulting fiber morphology were investigated. The analysis shows that in a multiple variable process like electrospinning, the interaction between the different processing parameters played an important role, rather than one parameter separately in obtaining desired nanofibers. Knowing the relative combined effects of processing parameters on fiber morphology should be useful for process control and prediction of electrospun fiber quality as it has been demonstrated in this study. © 2013 Society of Plastics Engineers. Source

Yang M.,Donghua University | Yan K.,Donghua University | Yan K.,Key Laboratory of Science and Technology of Eco Textile | Zhou A.,Donghua University
Journal of Applied Polymer Science | Year: 2010

A Xe* 2 excilamp (λ = 172 nm) was applied to photo irradiate and surface modify wool fibers. The scanning electron microscopy (SEM) showed that the outer surface of the fibers was etched after excilamp treatment, and some microcracks emerged on the surface scales. X-ray photoelectron spectroscopy (XPS) analysis indicated that the excilamp-treated fibers possessed high concentration of oxygen, sulfur, and nitrogen, as well as increased hydrophilic groups, such as hydroxyl group, carboxyl group, and sulphur oxide species on the surface. Fourier transform infrared spectroscopy with attenuated total internal reflectance (FTIR-ATR) mode measurement showed that the sulphur oxide species was mainly composed of cysteic acid and S-sulphonate together with a small amount of cystine monoxide and cystine dioxide on the outer surface of excilamp-treated wool fiber. The contact angle of water on the excilamp-treated fiber decreased about 110°. Using an acid dye, deeper hue was achieved in dyeing the treated fibers, evidenced by improved relative color strength (K/S) and brightness (L*) values. And the directional friction effect (DFE) of the wool fibers in wet obviously decreased after the excilamp treatment. © 2010 Wiley Periodicals, Inc. Source

Nurwaha D.,Donghua University | Wang X.H.,Donghua University | Wang X.H.,Key Laboratory of Science and Technology of Eco Textile
Fibres and Textiles in Eastern Europe | Year: 2012

This study describes the application of intelligent control systems in textile engineering and how to use these approaches for developing a spun yarn quality prediction system. The Multilayer Perceptron Neural Network(MLPNN), Support Vector Machines(SVMs), the Radial Basic Function Network(RBFN), the General Neural Network(GNN), the Group Method of Data Handling Polynomial Neural Network (GMDHPNN) and Gene expression Programming (GEP), generally called intelligent techniques, were used to predict the count-strength-product (CSP). Fiber properties such fibre strength (FS), micronaire (M), the upper half mean length (UHML), fibre elongation(FE), the uniformity index (UI), yellowness (Y), grayness (G) and short fibre content (SFC) were used as inputs. The prediction performances are compared to those provided by the classical Linear Regression (LR) model. The SVMs model provides good prediction ability, followed by the GEP and LR models, respectively. Graphs illustrating the relative importance of fibre properties for CSP were plotted. Fiber strength (FS) is ranked first in importance as a contributor to CSP by the five models, while fibre elongation (FE) ranks second. By means of the yarn strength learned surfaces on fibre properties, the study shows how to control yarn quality using knowledge of fibre properties. Source

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