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Jin G.,Zhejiang Sci-Tech University | Jin G.,Zhejiang Industry Polytechnic College | Zhu C.,Modern Textile Processing Technology National Engineering Research Center
Sen'i Gakkaishi | Year: 2015

To improve the feasibility of developing melt blown nonwoven filtering material with given pore size specifications, the predictive power of a back-propagation (BP) artificial neural network (ANN) that takes the processing parameters as its inputs for pore size and its distribution, characterized by the variation coefficient of pore size, was investigated. Twenty-seven samples of melt blown nonwoven were produced and their images were collected using the scanning electron microscopy (SEM) method. The pore sizes were measured using digital image processing technology in which maximum entropy thresholding image segmentation based on a genetic algorithm was adopted. Seven BP ANN models were constructed by varying the number of neurons in the hidden layer. Metering pump frequency, mesh belt frequency, and the distance from die to collector (DCD) were chosen as the inputs of BP ANN. The results show that BP ANN can effectively reflect the nonlinear relationship between the processing parameters, and the pore size and its distribution. The mean absolute percentage errors (MAPE) between the predicted values and the measured values of the 7 models are all below 5%. Among these 7 models, the one that contains 7 neurons in its hidden layer has the minimum predictive error. The ANN model has stronger predictive power than the multiple linear regression model.

Memon H.,Zhejiang Sci-Tech University | Wang N.,Zhejiang Sci-Tech University | Wang N.,Modern Textile Processing Technology National Engineering Research Center | Zhu C.,Zhejiang Sci-Tech University | Zhu C.,Modern Textile Processing Technology National Engineering Research Center
Journal of Fiber Bioengineering and Informatics | Year: 2015

The aim of this paper is to find the relation between material parameters and insertion loss of different fabrics using statistical analysis. This research primarily deals with the sound absorption analysis of different uncoated and coated woven textiles fabrics used for curtains as household textiles. The analysis of surface morphology was done by scanning electron microscopy to determine the significance of surface structure onto the sound insulation property. The acoustic properties were measured by reverberation method. The maximum sound insertion loss, minimum sound insertion loss, over all sound insertion loss and percentage improvement in sound insertion loss has been discussed and suggested the woven textiles to be better sound absorber at higher frequencies. Moreover, the effect structural parameters on average sound insulation index were analyzed using SPSS software. The results revealed that warp density, linear density of warp yarn, thickness are influential parameters for the sound insulation property of uncoated fabric whereas the areal density, linear density of warp yarn and thickness influence the sound insulation property coated fabrics. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

Li Q.,Zhejiang Sci-Tech University | Zhang F.,Zhejiang Sci-Tech University | Jin X.,Zhejiang Sci-Tech University | Zhang S.,Zhejiang SANZHI Textiles Co. | Zhu C.,Modern Textile Processing Technology National Engineering Research Center
Sen'i Gakkaishi | Year: 2014

In the textile industry, an innovative approach to achieving a special color is to use only a few colored yarns to gain various fabric color effects. This technology is highly environmentally friendly and can greatly reduce wastewater pollution caused by fabric dyes. In practice, however, matching the color of a woven fabric with the targeted or desired color by interweaving colored yarns experimentally has been difficult. Base on the optical color mixing theory and previous research on pre-colored fiber blending, this paper proposes a new model with an adjustable parameter M to predict the mixed color of yarn-dyed fabric made of dope-dyed polyester filaments. Experiments were carried out on 96 fabric samples that were a single color in the warp direction and different colors in the weft direction. A best-fit algorithm was used to determine that the optimum value of M for the Stearns-Noechel (S-N) color prediction model was 0.021. Differences in the values of the predicted and actual colors as determined with a spectrophotometer were calculated for three models : K/S, log (K/S), and S-N. The predictions of these models showed that the average color differences of the optimized S-N model were lower than those of the other two fixed-formula models. In addition, the optimized S-N model has different prediction accuracy in different range of wavelengths. © 2014, Society of Fiber Science and Technology. All rights reserved.

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