Layered fabric systems with an electrospun nanofiber web layered onto a sandwich of woven fabric were developed to examine the feasibility of developing breathable barrier textile materials. Some parameters of nanofiber mats, including the time of electrospinning and the polymer solution concentration, were designed to change and barrier properties of specimens were compared. Air permeability, water vapor transmission, and water repellency (Bundesmann and hydrostatic pressure tests) were assessed as indications of comfort and barrier performance of different samples. These performances of layered nanofiber fabrics were compared with a well-known water repellent breathable multi-layered fabric (Gortex). Multi-layered electrospun nanofiber mats equipped fabric (MENMEF) showed better performance in windproof property than Gortex fabric. Also, water vapor permeability of MENMEF was in a range of normal woven sport and work clothing. Comparisons of barrier properties of MENMEF and the currently available PTFE coated materials showed that, those properties could be achieved by layered fabric systems with electrospun nanofiber mats.
Specific internal pore architectures are required to provide the needed biological and biophysical functions for fibrous scaffolds as these architectures are critical to cell infiltration and in-grows performance. However, the key challenging on evaluating 3D pore structure of fibrous scaffolds for better understanding the capability of different structures for biological application is not well investigated. This article reports a fast, accurate, nondestructive, and comprehensive evaluation approach based on confocal laser scanning microscopy (CLSM) and three-dimensional image analysis to study the pore structure and porosity parameters of Nano/Microfibrous scaffolds. Also a new method of making the fiber fluorescent using quantum dots (QDs) was applied before 3D imaging. Fibrous scaffolds with different porosity parameters produced by electrospinning and their 3D-pore structure was evaluated by this approach and the results were compared to results of capillary flow porometry. The pore structural properties measured in this approach are in good agreement with that measured by the capillary flow porometry (with significant level 0.05). Furthermore, the introduced approach can measure the pore interconnectivity of the scaffold.
Conductive textile yarns were prepared by a continuous vapor polymerization method; the application of polypyrrole by the continuous vapor polymerisation method used is designed for the easy adaptation into industrial procedures. The resultant conductive yarns were examined by longitudinal and cross-sectional views, clearly showing the varying level of penetration of the polymer into the yarns structure. It was found that for wool the optimum specific resistance was achieved by using the 400 TPM yarn with a FeCl 3 solution concentration of 80 g/L FeCl 3 to produce 1.69 Ω g/cm 2. For cotton yarn, the optimum specific resistance of 1.53 Ω g/cm 2 was obtained with 80 g/L of a FeCl 3 solution.
Electrospinning process can fabricate nanomaterials with unique nanostructures for potential biomedical and environmental applications. However, the prediction and, consequently, the control of the porous structure of these materials has been impractical due to the complexity of the electrospinning process. In this research, a theoretical model for characterizing the porous structure of the electrospun nanofibrous network has been developed by combining the stochastic and stereological probability approaches. From consideration of number of fiber-to-fiber contacts in an electrospun nanofibrous assembly, geometrical and statistical theory relating morphological and structural parameters of the network to the characteristic dimensions of interfibers pores is provided. It has been shown that these properties are strongly influenced by the fiber diameter, porosity, and thickness of assembly. It is also demonstrated that at a given network porosity, increasing fiber diameter and thickness of the network reduces the characteristic dimensions of pores. It is also discussed that the role of fiber diameter and number of the layer in the assembly is dominant in controlling the pore size distribution of the networks. The theory has been validated experimentally and results compared with the existing theory to predict the pore size distribution of nanofiber mats. It is believed that the presented theory for estimation of pore size distribution is more realistic and useful for further studies of multilayer random nanofibrous assemblies.
This paper describes a computer vision-based fabric inspection system implemented on a circular knitting machine to inspect the fabric under construction. The study consisted of two parts. In the first part, detection of defects in knitted fabric was performed and the performance of three different spectral methods, namely the discrete Fourier transform, the wavelet and the Gabor transforms were evaluated off-line. In the second part, knitted fabric defect-detection and classification was implemented on-line. The captured images were subjected to a defect-detection algorithm, which was based on the concepts of the Gabor wavelet transform, and a neural network (as a classifier). An operator encountering defects also evaluated the performance of the system. The fabric images were broadly classified into seven main categories as well as seven combined defects. The results of the designed system were compared with those of human vision.Circular knitting is one of the easiest and fastest ways (20 million stitches per minute) to produce cloth and textile pieces such as garments, socks, and gloves. The fabric roll is removed from the large diameter circular knitting machine, and then sent to an inspection frame. If inspection could be done on the machine, the need for 100% manual inspection would be eliminated [7].The existing defect-detection techniques can be classified into three different categories: statistical, spectral, and model-based. The defect-detection approach by Zhang and Bresee [21] is based on first-order statistics such as mean and standard deviation. The fabric image is divided into sub-blocks with the use of information obtained by auto-correlation. The use of a gray-level cooccurrence matrix of the image is based on second-order statistics [2]. However, these statistical techniques are not useful for the detection of those textured defects whose statistical features, namely the first-and secondorder moments, are significantly close to that of defectfree textured regions [13]. A high level of quality assurance requires identification of such defects, and therefore techniques based on spectral features have been investigated in the literature.Textured materials, such as woven and knitted fabrics, possess strong periodicity due to the repetition of the basic weaving pattern. Therefore spectral techniques using a discrete Fourier transform [4,19], optical Fourier transform [17], and windowed Fourier transform [3] have been used to detect woven fabric defects. Escofet et al. [8] have used the angular correlation of the Fourier spectra to evaluate fabric web resistance to abrasion. and Chan and Pang [4] have used a Fourier transform to detect fabric defects. Ravandi and Toriumi [16] have also used Fourier transform analysis to measure fabric appearance. Escofet et al. [9] have used a bank of multi-scale and multi-orientation Gabor filters for the detection of local fabric defects. Kumar and Pang [12] have demonstrated another approach to fabric defect detection using real Gabor functions. Jasper et ...
The aim of this paper was to predict the needle penetration force in denim fabrics based on sewing parameters by using the fuzzy logic (FL) model. Moreover, the performance of fuzzy logic model is compared with that of the artificial neural network (ANN) model. The needle penetration force was measured on the Instron tensile tester. In order to plan the fuzzy logic model, the sewing needle size, number of fabric layers and fabric weight were taken into account as input parameters. The output parameter is needle penetration force. In addition, the same parameters and data are used in artificial neural network model. The results indicate that the needle penetration force can be predicted in terms of sewing parameters by using the fuzzy logic model. The difference between performance of fuzzy logic and neural network models is not meaningful ( RFL=0.971 and RANN=0.982). It is concluded that soft computing models such as fuzzy logic and artificial neural network can be utilized to forecast the needle penetration force in denim fabrics. Using the fuzzy logic model for predicting the needle penetration force in denim fabrics can help the garment manufacturer to acquire better knowledge about the sewing process. As a result, the sewing process may be improved, and also the quality of denim apparel increased.
This research investigates the effect of weave design and fabric weft density on the bagging behavior of cotton woven fabric interpreted in terms of fabric mechanical properties using FAST system. The results of this research show that the shear rigidity and formability significantly increased with weft density, whereas most fabric bagging parameters including bagging fatigue, resistance, hysteresis, and residual bagging hysteresis decreased accordingly. However, the weft density has no significant influence on residual bagging height. Moreover, the result of this work revealed that cotton fabric with a twill 2/2 structure exhibited the lowest shear rigidity and the highest formability which, in turn, led to the highest bagging parameters including bagging fatigue, resistance, hysteresis and residual bagging hysteresis, and the lowest residual bagging height among plain, twill 3/1, and hopsack weave designs.
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