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Fabric structural parameters play an important role on the thermal comfort of clothing. The purpose of this paper is to investigate the effect of weave pattern and also the length of warp float in each weave pattern on the thermal properties of woven fabrics.

Cotton woven fabrics with 23 different weave patterns were produced with identical linear densities of warp and weft yarns as well as constant warp and weft nominal densities. Thereafter, their thermal properties were studied.

Statistical analysis demonstrated that the weave pattern significantly influences on the thermal properties of woven fabrics. Plain fabric exhibited the lowest thermal resistance and the highest thermal conductivity, and hopsack 2/2(4) weave fabric demonstrated the highest thermal resistance and the lowest thermal conductivity. Moreover, except hopsack (4) weave fabric, in all weave patterns, the length of warp float had a significant effect on the thermal characteristics of the fabrics, as increasing the warp float led to increase in the thermal resistance of the fabrics.

Weave pattern as one of the structural parameters of the fabric has a determinant role on the thermal properties of fabric and subsequently, the comfort of clothing produced from it. Owing to the lack of investigation in this area, this research considers the effect of weave pattern and the length of warp float in each weave pattern on the thermal properties of woven fabrics.

The purpose of this paper is to develop a computerized program that can recognize woven fabric structures and simultaneously use the obtained data to 3D re‐visualize the corresponding woven fabric structures.

A 2D bitmap image of woven fabric was initially acquired using an ordinary desktop flatbed scanner. Through several image‐processing and analysis techniques as well as recognition algorithms, the weave pattern was then identified and stored in a digital format. The weave pattern data were then used to construct warp and weft yarn paths based on Peirce's geometrical model.

By combining relevant weave parameters, including yarn sizes, warp and weft densities, yarn colours as well as cross‐sectional shapes, a 3D image of yarns assembled together as a woven fabric structure is produced and shown on a screen through the virtual reality modelling language browser.

Woven fabric structures can now be recognised and simultaneously use the obtained data to 3D re‐visualize the corresponding woven fabric structures.

The purpose of this paper is introducing the image processing technology used for fabric analysis, which has the advantages of objective, digital and quick response.

This paper briefly describes the key process and module of some typical automatic recognition systems for fabric analysis presented by previous researchers; the related methods and algorithms used for the texture and pattern identification are also introduced.

Compared with the traditional subjective method, the image processing technology method has been proved to be rapid, accurate and reliable for quality control.

The future trends and limitations in the field of weave pattern recognition for woven fabrics have been summarized at the end of this paper.

Testing the effect of machine washing and drying on dimensional stability produces information about the fabric types that satisfy consumers during end use. At present, it is a known fact that the weave patterns affect the dimensional stability property of woven fabrics. But the essential requirement is to determine the magnitude of this effect for weave types and establish the proper weave types for end use in definite tolerances. The purpose of this paper is to investigate the dimensional stability properties of 100 percent cotton woven fabrics as a function of weave type.

In total, 12 woven fabrics with different weave derivatives are woven with 100 percent cotton and Ne 30/1 combed ring spun yarn for this investigation. These samples are then washed and dried according to domestic washing and drying standard test procedures. The shrinkage values are measured and then expressed as a percentage of the initial dimensions.

It was observed that weave pattern has a significant effect on the dimensional behavior of woven fabrics. Weave patterns with a high number of interlacings have lower shrinkage values. At the same time, lower yarn crimp values restricted the fabric shrinkage and resulted in better dimensional stability. According to one way ANOVA results, the effect of weave type on dimensional stability is found to be significant (p<0.01). In addition to these, Pearson correlation analysis showed that there is an important, positive and fair relationship between the number of washing cycles and total shrinkage.

The study covers 100 percent cotton woven fabrics with one type of warp and weft sett. The only finishing treatment applied to the sample fabrics was desizing. No dyeing was carried out.

Understanding the magnitude of the effect of weave type on dimensional stability of cotton woven fabrics produces more knowledge about products which satisfy the customers with respect to dimensional stability during usage.

This paper aims to propose a biologically inspired processing architecture to recognize and classify fabrics with respect to the weave pattern (fabric texture) and yarn color (fabric color).

By using the fabric weave patterns image identification system, this study analyzed the fabric image based on the Hierarchical-MAX (HMAX) model of computer vision, to extract feature values related to texture of fabric. Red Green Blue (RGB) color descriptor based on opponent color channels simulating the single opponent and double opponent neuronal function of the brain is incorporated in to the texture descriptor to extract yarn color feature values. Finally, support vector machine classifier is used to train and test the algorithm.

This two-stage processing architecture can be used to construct a system based on computer vision to recognize fabric texture and to increase the system reliability and accuracy. Using this method, the stability and fault tolerance (invariance) was improved.

Traditionally, fabric texture recognition is performed manually by visual inspection. Recent studies have proposed automatic fabric texture identification based on computer vision. In the identification process, the fabric weave patterns are recognized by the warp and weft floats. However, due to the optical environments and the appearance differences of fabric and yarn, the stability and fault tolerance (invariance) of the computer vision method are yet to be improved. By using our method, the stability and fault tolerance (invariance) was improved.

The aim of the present study is to investigate the effect of fabric weave structure on air permeability and its relation with the garment ventilation.

For this purpose, five groups of cotton/polyester shirting fabrics with plain, T2/1, T2/2, T3/1 and T3/3 weave structures were studied. In order to evaluate ventilation, the garment samples were prepared in different sizes, so that the thickness of the air gap formed between the garment and the body simulator varies by zero, 1.5, 1.2 and 2.9 cm. The effect of wind and its speed (1, 2 and 3 m/s) on clothing ventilation has also been evaluated.

The results indicated that the rise of wind speed and air gap thickness, due to the increased convective heat transfer, would diminish the air gap temperature of clothing and improves its ventilation. In addition, the fabric weave pattern influences the air ability to pass through the fabric, thus affecting the ventilation capability of the garment.

Garments made of fabrics with higher structural firmness, such as the plain, not only have lower air permeability, but also has weaker ventilation capability.

The purpose of this paper is to theoretically compute and predict the needle penetration force (NPF) in woven denim fabrics with twill 3/1 weave pattern on the basis of geometrical, physical, and mechanical properties of yarns and fabric, and characteristics of sewing needle.

To predict the NPF by mathematical relations, the proposed models by Stylios and Xu (1995) and Lomov (1998) are extended for a twill woven structure. The NPF is calculated based on resistance forces due to yarn tensile elongation, yarn resistance to bending in the near of the sewing needle while the needle penetrates into the fabric, friction between weft and warp yarns, needle profile shape, and friction between sewing needle and yarns. In order to evaluate the obtained results, nine different denim fabric samples are produced, and five sewing needles with different sizes are used. The NPF is measured on the Instron tensile tester to simulate sewing process.

The results show that there is a good relationship between the predicted and experimental values of the NPF (R2=0.831, MSE=0.079, and MAPE=9.51 percent). Moreover, it is found that the performance of developed model to predict the NPF for needle sizes of 80, 90, 100, and 110 (Nm) is better than that of needle size of 120 (Nm). Generally, the developed theoretical model can predict the NPF in fabrics with twill 3/1 weave pattern.

The fabrics with twill weave pattern have a complicated structure than plain pattern. So, in this research work, the NPF of denim fabric with twill 3/1 weave pattern was theoretically predicted on the basis of yarn elongation, changing of yarn bent shape in the near of the sewing needle, and friction between warp and weft yarns. The NPF was measured in the successive cycle loading conditions similar to sewing machine process by using a designed and constructed instrument, which is mounted on the Instron tensile tester.

The purpose of this paper is to theoretically calculate and predict the needle penetration force (NPF) in woven denim fabrics with twill 3/1 weave pattern, based on the geometrical, physical, and mechanical properties of fabric and constituent weft and warp yarns.

In order to calculate the NPF by mathematical relations, the suggested model by Stylios and Xu (1995) is extended. While the needle penetrates into the fabric, the NPF is calculated on the basis of yarn tensile elongation, needle profile shape, and friction between needle and yarns. To accurately evaluate the developed model, nine different commercial denim fabric samples with various weft densities and linear density of weft yarns are produced, also five needles with different sizes are utilized. The NPF is measured in the successive cycle loading conditions similar to sewing machine process by using a designed and constructed instrument, which mounted on the Instron tensile tester.

Comparing the predicted and experimental values indicated that the accuracy of model to predict the NPF is partly acceptable (R2=0.734). To improve the developed model, in addition to the forces which applied on needle due to yarn elongation, the other forces such as friction between weft and warp yarns, and yarn resistance to bending in the near of the sewing needle, which resist against needle penetration, can be considered.

The fabrics with twill weave pattern have a complicated structure than plain pattern, therefore, in this paper the NPF of denim fabrics with twill 3/1 weave pattern were theoretically calculated based on yarn elongation. The spacing between centers of yarns in these fabrics is obtained by deriving a new formula. The NPF is measured on the Instron tensile tester to simulate sewing process.

This paper aims to study the optical properties of woven fabrics.

The current study was carried out to objectively evaluate the luster of a group of woven fabrics with different weave structures and weft densities, with the aid of a goniophotometer. The results obtained from the objective luster measurement were validated by a set of pair comparison subjective tests using Thurstone’s law of comparative judgment.

The proper correlation with the R² value of more than 0.96, between subjective and objective tests, confirmed the reliability and accordance of objective results with the human perception of luster. Statistical analysis of the luster results clarified that the effect of fabric structural parameters such as weave structure and weft density are significant at the confidence range of 95 per cent. The highest luster index was achieved for the twill 3/1 weave structure and the lowest luster belonged to the plain pattern. In addition, an increase in the density of the fabric leads to better luster. Moreover, it was concluded that the surface roughness affects the luster. A rise in the roughness value of the woven fabric causes reduction in its luster property.

Optical properties of woven fabrics, which are mainly attributed through the measurement of luster, are important for qualifying the aesthetic characteristics of the fabrics with various weave structures. Bearing in mind the influence of fabric surface properties on the aesthetic features of cloths, obtaining information in this field is a guide for selecting the suitable fabric for various end uses.

The aim of this study was to understand the stick-slip properties of single and multiple yarn pull-out in dry and treated polyester satin woven fabric in boundary regions.

Polyester satin pattern woven fabric was used to conduct the pull-out tests in order to examining the kinetic region of the force-displacement curve. Data generated from this research help the authors to obtain stick-slip force and accumulative retraction force.

It was found that stick-slip force and accumulative retraction force depend on the number of pulled ends in the fabric, fabric sample dimensions and softening treatments. Stick-slip forces of polyester satin fabric in the multiple yarn pull-out test were higher than those of the single yarn pull-out test. Stick-slip force in single and multiple yarn pull-out tests in the dry polyester satin fabric was generally higher than those of the softening treated polyester satin fabric. In addition, the warp directional single and multiple yarn stick-slip and accumulative retraction forces in the dry and softening treated polyester fabrics were generally higher than those in the weft direction in the fabric edges due to fabric density. On the other hand, the amount of stick-slip force was related to the number of interlacement points in the fabric, whereas the amount of accumulative retraction force was related to fabric structural response.

The mechanism of stick-slip and accumulative retraction force of dry-treated polyester satin pattern woven fabrics were explained. This research could be valuable for development of multifunctional fabrics in technical textiles and ballistic.