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ISRN Textiles
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  This is an Open Access Journal Open Access journal
     ISSN (Online) 2314-6389
     Published by ISRN International Scholarly Research Network Homepage  [96 journals]
  • Performance Optimization, Prediction, and Adequacy by Response Surfaces
           Methodology with Allusion to DRF Technique

    • Abstract: The RSM introduces statistically designed experiments for the purpose of making inferences from data. The second-order model is the most frequently used approximating polynomial model in RSM. The most common designs for the second-order model are the 3 factorial, Doehlert, Box-Behnken, and CCD. In this Box and Behnken design of three variables is selected as a representative of RSM and 70 : 30 polyester-wool DRF yarn knitted fabrics samples as a process representative. The survey reveals that second-order model is the most frequently used approximating polynomial model in RSM. The Box-Behnken is the most suited design for optimization and prediction of data in textile manufacturing and this model is well-suited for DRF technique yarn knitted fabric. The trend was as higher wool fiber length shows higher fabric weight, abrasion, and bursting strength, correlation of TM was not visible; however, role of strands spacing is found dominant in comparison to other variables; at 14 mm spacing it shows optimum behaviors. The optimum values were weight (gms/mt2) 206 at length 75 mm, TM 2.5 and 14 mm spacing, abrasion (cycles) 1325 at length 70 mm, TM 2.25 and 14 mm spacing, bursting (kg/cm2) 14.35 at length 70 mm, and TM 2.00 and 18 mm spacing. A selected variables, fiber length, TM, and strand spacing, have substantial influence. The adequacies of response surface equations are very high. The line trends of knitted fabric basic characteristics were almost the same for actual and predicted models. The difference (%) was in range of 1.21 to −1.45, 2.01 to −7.26, and 17.84 to −6.61, the accuracy (%) was in range of 101.45 to 98.79, 107.27 to 97.99, and 106.61 to 82.16, and the Discrepancy Factor (-Factor) was noted to be 0.016, 0.002, and 0.229 for weight, abrasion, and bursting, respectively, between actual and predicted data. The -estimation factors for actual and predicted data were that (i) the ratio were in range of 1.01 to 0.99, 1.02 to 0.93, and 1.22 to 0.94 for weight, abrasion, and bursting, respectively, (ii) the multiple-ratio was in range of 1.26 to 0.86, (iii) the ratio product was in range of 1.22 to 0.92, and (iv) the toting ratio was in range of 1.02 to 0.94.
      PubDate: Tue, 04 Mar 2014 14:27:29 +000
       
  • Morphology, Thermal, and Mechanical Characterization of Bark Cloth from
           Ficus natalensis

    • Abstract: The United Nations Educational, Scientific and Cultural Organization (UNESCO) proclaimed in 2005 that Ugandan bark cloth is largely produced from mutuba tree (Ficus natalensis) as a “Masterpiece of the Oral and Intangible Heritage of Humanity.” An exploratory investigation of bark cloth a nonwoven material produced through a series of pummeling processes from mutuba tree in Uganda is fronted as a prospective engineering natural fabric. Bark cloth was obtained from Ficus natalensis trees in Nsangwa village, Buyijja parish in Mpigi district, Central Uganda. The morphology of the fabric was investigated using scanning electron microscope (SEM). thermal behavior of the fabric was studied using thermagravimetric analysis (TGA) and differential scanning calorimetry (DSC). Fourier transform infrared spectroscopy was used to evaluate the surface functional groups. The fabric was subjected to alkaline treatment for six hours at room temperature in order to study the change in fabric thermal properties so as to set a base for applications in biodegradable composites. Findings show that the natural nonwoven fleece is stable below 200°C; alkaline treatment positively influences the thermal behavior by increasing the onset of cellulose degradation temperature. The fabric morphology showed that it is made up of fairly ordered microfibers which can be beneficial for nanocomposites.
      PubDate: Sat, 31 Aug 2013 13:50:40 +000
       
  • Measuring Clothing Color and Design Symbolism Preferences and Purchase
           Intentions of Asian Indian Females at Different Levels of Acculturation

    • Abstract: The purpose of this study was to develop a reliable and valid instrument to measure color, design clothing preferences, and purchase intentions of Asian-Indian female consumers; secondarily, to determine if westernized clothes with Asian-Indian ethnic dress elements might be purchased more often than westernized clothing with design attributes primarily symbolic of American culture at different levels of acculturation. The instrument included a modified acculturation scale, limited demographics, and the developed Clothing Preferences and Purchase Intention Instrument. The instrument consisted of four components: Color Symbolism and Purchase Intention, Design Symbolism and Purchase Intention, Symbolic Attributes Scale, and Clothing Preference and Purchase Intention for Mainstream American versus Asian-Indian Inspired. All of the scales had high reliability. Of the 30 colors in the instrument, red, magenta, orange gold, yellow, cobalt blue, and purple were symbolic of Asian-Indian dress; hunter green, navy blue, baby blue, and blue were considered western colors. Neutral colors were eliminated. Nine of the 27 tunics in the instrument were highly indicative of Asian-Indian clothing; 11 were indicative of westernized clothing. Secondarily, Asian-Indians preferred and showed intent to purchase westernized clothing with colors and designs associated with their native country’s traditional dress regardless of acculturation.
      PubDate: Thu, 18 Jul 2013 09:03:43 +000
       
  • Near-Infrared Spectroscopy for Anticounterfeiting Innovative Fibers

    • Abstract: Near-infrared (NIR) spectroscopy has gained increased attention for the qualitative and quantitative evaluation of textile and polymer products. Many NIR instruments have been commercialized to identify the natural and synthetic fibers; however, there is a strong need to have NIR database of these high-performance fibers to detect contraband textile materials rapidly and quantitatively. In this study, NIR spectra of PLA, Kevlar, Spandex and Sorona woven fabrics were collected and studied by several calibration models to identify the fibers. The results indicated that these four innovative fibers had been successfully distinguished by their NIR spectra in combination with preprocessing of 1/X transformation, SNV, and 2nd Savitzky-Golay derivative as well as principal-component-analysis (PCA-) based chemometric methods. Our promising results suggest that NIR spectroscopy is an effective technique to anticounterfeit innovative fibers.
      PubDate: Sun, 23 Jun 2013 14:10:47 +000
       
  • Bleaching Process Investigation of Tunisian Dromedary Hair

    • Abstract: Successful bleaching of pigmented fibres was, generally, evaluated by a maximum whiteness, a minimum yellowness, and less damage to the bleached fibers. A review of the literature reveals that many studies on pigmented fibre bleaching are concerned with improving the whiteness and mechanical properties of bleached fibres. In this study, we investigate the effects of the hydrogen peroxide concentration, bleaching time, and clarification bath on the bleaching efficiency of Tunisian dromedary hair. It was showed that 30 min bleaching time gives better result in term of whiteness. However, an increased bleaching time gives an excessive damage to the bleached fibers. Further, the damage incurred by the dromedary hair was more important than that for wool, as is shown by the tenacity results. We found that oxalic acid, which is used for rinsing dromedary hair (after bleaching), provides improved results in term of whiteness obtained with bleaching. Certainly, oxalic acid made it possible to remove the maximum of iron remaining on fibre after bleaching. Bleaching methods demonstrate the excessive damage incurred by the fibre when using hydrogen peroxide particularly with raise concentration. This damage leads to adverse effects on the tenacity fibre.
      PubDate: Tue, 04 Jun 2013 15:09:56 +000
       
  • Determination of Fiber Contents in Blended Textiles by NIR Combined with
           BP Neural Network

    • Abstract: Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.
      PubDate: Sun, 19 May 2013 08:54:58 +000
       
 
 
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