Nilgün Özdil

Moisture management and thermal absorptivity properties of double-face knitted fabrics

Double face fabrics are used to ensure better clothing comfort in sports and active wears. High moisture transfer properties of these fabrics affect their comfort properties and make them more functional. In this study, in order to investigate the moisture management properties of the double face fabrics, a special knitting structure, which has different or same yarn combinations in the face and back sides, was used. The selected yarns were cotton—cotton, cotton—polypropylene, polypropylene—cotton and polypropylene—polypropylene for face and back sides respectively. Moisture management properties and the changes of the thermal absorptivity values, which determine the warm-cool feeling, of the produced fabrics were determined and statistically analyzed. All measurements were done both in dry and wet conditions. The results indicate that, the polypropylene (inner)-cotton (outer) fabric has better moisture management property, provides high levels of comfort and can be preferred for summer, active and sports wear.

The Effect of Fiber Properties on the Characteristics of Spliced Yarns: Part II: Prediction of Retained Spliced Diameter

The objective of the second part of the study is to investigate the retained spliced diameter with regard to splicing parameters and fiber and yarn properties. In order to analyze the effects of the fiber and yarn properties on the retained spliced diameter of ring spun cotton yarns, the yarns were produced from eight different cotton types in three yarn counts (29.5, 19.7 and 14.8 tex) and three different twist coefficients (αtex 3653, α tex 4038, αtex 4423). To investigate the effects of splicing parameters on the retained spliced diameter, opening air pressure, splicing air pressure and splicing air time were set according to an orthogonal experimental design. The retained spliced diameter was calculated and predicted by using an artificial neural network (ANN) and response surface methods. Analyses showed that ANN models are more powerful compared with response surface models in predicting the retained spliced diameter of ring spun cotton yarns.

The Effect of Fiber Properties on the Characteristics of Spliced Yarns Part I: Prediction of Spliced Yarns Tensile Properties

In this study, the effects of splicing parameters, fiber and yarn properties on the tenacity and elongation of spliced yarns were investigated in detail. For this purpose, yarns from eight different cotton types, having three different counts (29.5, 19.7 and 14.8 tex) and three different twist coefficients (α tex 3653, αtex 4038, αtex 4423) were produced. Fiber properties measured using an Advanced Fiber Information System fiber tester were evaluated. Artificial neural network and response surface models were used to analyze spliced yarn tenacity and elongation as dependent variables. As independent variables, fiber properties together with the machine settings such as opening air, splicing air and splicing time, yarn twist and yarn count were chosen. As a result of the study, equations and neural network models that predict the tenacity and elongation of the spliced yarns were obtained. The obtained equations and models are statistically important and have high coefficient of multiple determination (R 2).

Sensorial Comfort of Textile Materials

In order to find a method for the sensational evaluation of textiles, the concept of “fabric hand” is commonly used (Makinen et al., 2005). Understanding and measuring consumer preferences have opened up an important field of interest in recent researches in textile industry known as “handle” or in a broader sense “skin sensational wear comfort” or “tactile comfort” (Bensaid et al., 2006; Das & Ishtiaque, 2004) which refers to the total sensations experienced when a fabric is touched or manipulated in the fingers. Since fabric handle is based on peoples subjective preferences, obviously it can mean different things to different people and consumers having different backgrounds. The preferences for certain fabric types are diverse and, in extreme cases, even opposite (Pan et al., 1988). Hand influences consumers’ preferences and their perception of the usefulness of the product, and consequently retailer’s saleability of the apparel. This fabric property is critical to manufacturers, garment designers, and merchandisers in developing and selecting textile materials, especially the textiles intended for use in apparel (Kim & Slaten, 1999; as cited in Pan & Yen, 1992; as cited in Pan et al., 1993).

Analysis of Abrasion Characteristics in Textiles

Wear in textile materials is one of a limited number of fault factors in which an object loses its usefulness and the economic implication can be of enormous value to the industry. The terms wear and abrasion are sometimes confused. Wear is a very general term covering the loss of material by virtually any means. As wear usually occurs by rubbing together of two surfaces, abrasion is often used as a general term to mean wear (Brown, 2006).

Application of artificial neural network (ANN) for the prediction of thermal resistance of knitted fabrics at different moisture content

Abstract Thermal resistance of the fabrics is one of the decisive parameters in terms of comfort; however it can change due to wetting. Therefore, thermal resistance of wetted fabric is important for comfort performance of garments. In recent years, artificial neural networks (ANN) have been used in the textile field for classification, identification, prediction of properties and optimization problems. ANNs can predict the fabric thermal properties by considering the influence of all fabric parameters at the same time. In this study, ANNs were used to predict thermal resistance of wetted fabrics. For this aim, two different architectures were experienced and high regression coefficient (R2) between the predicted (training and testing) and observed thermal resistance values were obtained from both models. The obtained regression coefficient values were over 90% for both models. Then it can be said that ANNs could be used for predicting thermal resistance of wetted fabrics successfully.

Prickle and handle properties of fabrics produced from specialty animal fibers

Specialty animal fibers are commercially valuable since they significantly enhance the comfort properties of apparel products. As they help to create niche products, use of special animal fibers has increased significantly. There are many types of specialty fibers, such as cashmere, mohair, alpaca, and angora rabbit fibers, that are natural, ecological, and sustainable. Each of them has distinguishing properties and increases the added value of garments. In this work, five different specialty animal fibers and wool were sourced from four different countries. Yarns were spun using two different spinning systems. In the first method, to spin the yarns in a short staple spinning system, animal fibers were blended with viscose fibers. In the second method, to produce fabrics from 100% animal fibers, core yarn spinning system was used. Later, the yarns were knitted in a single jersey structure. After the production of all fabrics, handle properties such as surface friction, stiffness, and prickliness were tested by using both subjective and objective measurement techniques. To measure inherent fabric prickle, a new objective measurement technique was developed. Subjective and objective evaluation results highly correlate and it is pointed out that the new technique could be an alternative method to test fabric prickliness. Fabrics produced from silk, angora, and cashmere fibers are found to have the smoothest, softest, and least prickly surfaces among the other researched fibers.