Effect of polypropylene fiber cross sectional shapes on some structural/mechanical fiber properties and compressibility behaviour of plain knitted fabrics

Synthetic fibers are generally produced with circular cross sectional shapes. Other cross sectional shaped fibers such as trilobal, triangular, hollow and pentagonal fibers are also produced to improve some properties of fibers and fabrics such as lustre, handle, wicking rate, strength, stiffness and bulkiness. In this research we aimed to investigate compressional behaviours of fabrics knitted from polypropylene fibers having three different cross sectional shapes; namely circular, trilobal and triangular. Morphological, structural and mechanical properties of produced fibers were evaluated by using scanning electron microscopy, X-ray diffractometry, differential scanning calorimetry and tensile tester, respectively. In terms of structural and mechanical properties, no significant differences were found related to fiber cross sectional shapes. Then, plain knitted farbrics were produced and compressional properties of these fabrics were investigated. Fabrics knitted from trilobal fibers showed the highest compressibility properties and it is followed by fabrics which are produced from triangular and circular fibers.     

Effect of using cold plasma on dyeing properties of polypropylene fabrics

The low temperature plasma (LTP) technique is used widely to modify polymer and textile materials. This paper describes the development of a plasma system for textile treatment. Polypropylene (PP) has a very low value of the surface free energy (approximately 20–25 mJ/m²). Due to low surface energy, Polypropylene has very weak hydrophilic properties. By controlling the plasma variables, such as the nature of gas, the discharge power, the pressure and the exposure time, a great variety of surface effects can be generated. In this paper, we report the effect of cold plasma of O₂ and N₂ gases at various time of exposure on the dyeing and physical properties of PP fabrics. The results show a significant increase in the color depth upon dyeing after treating PP fabrics with low temperature plasma of O₂ and N₂. For comparing the amount of fabrics dye exhaustion, we have used reflective spectrophotometer. The morphology of the modified surfaces has also been investigated using scanning electron microscopy (SEM). And also FTIR was used to examine the functional groups of the corresponding samples.     

Assessment of the surface roughness of cotton fabrics through different yarn and fabric structural properties

The effects of some yarn properties (i.e. type, count, twist level, ply number, unevenness and crimp) and fabric constructional properties (i.e. cover, thickness and balance) on surface roughness values of cotton woven fabrics were investigated. A general overview of the results showed that surface roughness values of fabrics were affected from yarn and fabric properties and the effects were related to fabric balance, fabric cover (not cover factor), fabric thickness and crimp values of yarns in fabric structures. Surface roughness values of fabrics decreased as yarn fineness and yarn twist levels increased but as yarn ply number decreased. Also, surface roughness values gradually decreased from open-end yarn constituting fabrics to combed yarn constituting fabrics. Results showed that different properties of yarns caused changes in yarn crimps in fabric structure and also governed the changes in fabric balance, as well as changes in roughness of fabric surfaces. The changing properties of yarns and impact of these properties on fabric construction affected the formation of cotton fabric surfaces from smooth to coarse.     

Engineering design of woven fabrics using non-traditional optimization methods: A comparative study

This work aims to design woven fabrics with desired quality at optimum manufacturing cost by choice of suitable weaving parameters such as count, crimp and thread spacing of warp and weft yarns. To fulfill this goal, we endeavor to devise search based non-traditional optimization methods such as genetic algorithm, particle swarm optimization and simulated annealing for efficiently finding the appropriate combination of weave parameters. The quick response capability of the non-traditional optimization methods would benefit the fabric manufacturers for efficient determination of the required weaving parameters to produce the engineered fabrics. The experimental validation confirms that the particle swarm optimization is most suitable technique for engineering design of woven fabrics.     

A comparative study on performance properties of yarns and knitted fabrics made of biodegradable and conventional fibers

Biodegradable products are parts of a natural cycle. The biopolymers and the fibers that can be produced from them are very attractive on the market because of the positive human perception. Therefore, PLA being a well known biodegradable fiber and some conventional fibers were selected for the current study to examine the differences between them and to emphasize the importance of biodegradability beside fabric performance. 14.8 tex (Ne 40/1) combed ring spun yarns produced from biodegradable fiber PLA, new generation regenerated fibers Modal and Tencel, synthetic and blends 50/ 50 % cotton/polyester and 50/50 % viscose/polyester, polyester were selected as yarn types and by using these yarns, six knitted fabrics were produced and some important yarn and fabric properties were compared. In this context, moisture and the tensile behavior of yarns and pilling, bursting strength, air permeability and moisture management properties of the test fabrics are discussed.     

Dimensional and physical properties of plain knitted fabrics made from 50/50 bamboo/cotton blended yarns

In this study, the dimensional and some physical properties of plain knitted fabrics made from 50/50 bamboo/cotton blended yarns are investigated. In order to see the differences and similarities, the results are then compared with those for similar fabrics knitted from 50/50 conventional viscose/cotton and 50/50 modal/cotton blended yarns. Each fabric type was produced with three different stitch lengths. After all fabrics were dyed under identical dyeing conditions, they were subjected to dry and full relaxation treatments. For dimensional properties of fabrics, course, wale and stitch densities were measured. Then, by calculating statistically best-fit lines passing both through the experimental points and the origin, dimensional constants i.e. k values were predicted in terms of the fiber types. The result show that each fabric type knitted from bamboo/cotton, viscose/cotton and modal/cotton blended yarns behaves in a similar manner. However, in both dry and fully relaxed states, the modal/cotton knitted fabrics tend to have slightly higher k values than the bamboo/cotton and viscose/cotton knitted fabrics. For physical properties, fabric weight per unit area, thickness, bursting strength, air permeability and pilling were evaluated. The results show that the weight, thickness and air permeability values are independent of the fiber type. Plain knitted fabrics from modal/cotton blended yarns have the highest bursting strength values. Plain knitted fabrics from bamboo/cotton blended yarns tend to pill less.     

Investigation on air permeability of finished stretch plain knitted fabrics. I. Predicting air permeability using artificial neural networks

Air permeability is one of the most important utility properties of textile materials as it influences air flow through textile material. Air permeability plays a significant role in well-being due to its influence on physiological comfort. The air permeability of textile materials depends on their porosity. There are a lot of structural properties of textile materials also operating parameters (knitting+finishing) influencing air permeability and there are also statistically significant interactions between the main factors influencing the air permeability of knitted fabrics made from pure yarn cotton (cellulose) and viscose (regenerated cellulose) fibers and plated knitted with elasthane (Lycra) fibers. Two types of artificial neural networks (ANNs) model have been set up before modeling procedure by utilizing multilayer feed forward neural networks, which take into account the generality and the specificity of the product families respectively. A virtual leave one out approach dealing with over fitting phenomenon and allowing the selection of the optimal neural network architecture was used. Moreover this study exhibited that air permeability could be predicted with high accuracy for stretch plain knitted fabrics treated with different finishing processes. Within the framework of the work presented, ANNs were applied to help industry to adjust the operating parameter before the actual manufacturing to reach the desired air permeability and satisfy their consumers.     

Simulation of the weave structural design of synthetic woven fabrics

This study surveys the basic procedure of data base system of the fabric structural design which can be linked with existing pattern design and garment design CAD systems. For this purpose, the theoretical and empirical equations related to the fabric structural design are analyzed and discussed with various fabric specimens. The fabric structural parameters such as weave density coefficient, cover factor and yarn density coefficient of various kinds of fabrics are calculated using the empirical equations. These calculated fabric structural parameters of many kinds of polyester and nylon fabrics are compared and discussed with weave pattern, and materials such as polyester and nylon. Furthermore the difference between fabric structural parameters calculated by empirical equations are analyzed with polyester and nylon fabrics as a basic study for data base system of the fabric structural design. Finally, the weave density coefficients of polyester and nylon fabrics were analysed and discussed with shrinkages of dyeing and finishing processes, and also surveyed according to the weaving company and weave structural parameters such as weave pattern and denier.     

A study on the compression behavior of spacer fabrics designed for concrete applications

Spacer fabrics have been used in many areas varying from medical applications to protection applications. Especially the three dimensional characteristic of spacer fabrics presents different opportunities for special applications. The compression resistant characteristic of spacer fabrics is one of their main properties. In this research the compression behaviour of spacer fabrics designed for concrete applications has been investigated. The effects of some parameters such as spacer yarn material, pattern and threading on the compression behaviour of spacer fabrics have been studied. According to the test results it was found that the material, pattern and the threading of spacer yarns are important parameters for the compression characteristics of spacer fabrics. It was also observed that the location angle of spacer yarn and the amount of the spacer yarns influence the compression behaviour of spacer fabrics.     

A novel DDPSi-FR flame retardant treatment and its effects on the properties of wool fabrics

A novel flame retardant monomer DDPSi-FR containing organophosphorus and silicon was prepared using 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO), 4-hydroxybenzaldehyde (HBA), and 3-glycidoxypropyltrimethoxysilane (GPTMS). The chemical structure of DDPSi-FR was characterized by Fourier transform infrared spectroscopy (FT-IR) and X-ray photoelectron spectroscopy (XPS). Subsequently, after treating the wool fabrics, the effects of the monomer on the flame retardancy, thermal stability, and mechanical properties were studied. The flame retardant and thermal properties were evaluated by conducting vertical flame tests, limiting oxygen index (LOI) determination, and thermogravimetric analysis (TGA). The results showed that improved flame retardancy and thermal stability were achieved. Notably, the flame retardancy was retained even after 15 washing cycles. The mechanical properties were evaluated using the bursting strength, and the results indicated that DDPSi-FR treatment improved the breaking strength.     

Predicting properties of single jersey fabrics using regression and artificial neural network models

In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties were selected as independent variables together with wales’ and courses’ number per square centimeter. Firstly conventional ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35) and three different twist multipliers (α _e 3.8, α _e 4.2, and α _e 4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully. However, ANN models exhibit higher predictive power than the regression models.     

Dimensional,physical and thermal comfort properties of plain knitted fabrics made from modal viscose yarns having microfibers and conventional fibers

The dimensional, some physical and thermal comfort properties of the plain knitted fabrics having modal viscose microfibers in three different stitch lengths are investigated in comparison with the similar fabrics having conventional modal viscose fibers. The fabrics made from microfibers and conventional fibers exhibit different dimensional properties. The stitch density results and the dimensional constants calculated at the fully relaxed state reveal that the fabrics with microfibers tend to have lower shrinkage tendency than those with conventional fibers. The statistical results show that the fiber type (or fiber fineness) and the stitch length affect the some physical properties and all of the thermal comfort properties of the fabrics significantly. The bursting strength values of the fabrics with microfibers are observed to be slightly higher than those of the fabrics with conventional fibers. However, the difference between the bursting strength values of these fabrics is found to be statistically unimportant. The fabrics with microfibers reveal lower thickness and air permeability and, higher pilling tendency than those with conventional fibers. It is also observed from the thermal comfort results that the fabrics made from microfibers have higher thermal conductivity, thermal absorptivity and maximum heat flux values and, lower thermal resistance and thermal diffusivity values. Because of the higher thermal absorptivity and maximum heat flux values, the fabrics from microfibers provide cooler feeling when compared with those from conventional fibers.     

Study on the aging and yellowing properties of sebum-soiled cotton fabrics

The accumulation and autoxidation of residual oily soil from human sebum is regarded as a major reason for the aging and yellowing of clothing and household textiles. In this study, the yellowing degree of cotton fabrics soiled with human sebum components and aged under various environmental conditions (various temperatures, humidities and light intensities etc.) were investigated. The representative sebum components including olive oil, squalene, oleic acid and cottonseed oil were selected and their chemical changes were studied by FTIR analysis. The results showed that, olive oil, squalene, oleic acid and cottonseed oil could caused more significant fabric-yellowing than other components in human sebum, and the yellowing degree could become higher with increasing temperatures, humidities and/or light intensities of aging environments. The chemical structures of olive oil, squalene and oleic acid changed remarkably after aging treatment. The aging products contained hydroxyl groups and carbonyl groups, and the conjugated C=O group was considered to be the chemical species leading to the yellowing of sebum-soiled fabrics.     

Comparison of regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics

The aim of this study was to compare the response surface regression and adaptive neuro-fuzzy models for predicting the bursting strength of plain knitted fabrics. The prediction models are based on the experimental data comprising yarn tenacity, knitting stitch length and fabric GSM as input variables and fabric bursting strength as output/response variable. The models quantitatively characterize the non-linear relationship and interactions between the input and output variables exhibiting very good prediction ability and accuracy, with ANFIS model being slightly better in performance than the regression model.     

Effect of ultrasonic laundering on thermophysiological properties of knitted fabrics

The paper focuses on the application of ultrasonic energy in textile laundering. In recent years, there has been an increasing interest in ultrasonic energy application in textile industry; however, the effect of ultrasonic laundering on the thermophysiological properties of knitted fabrics has not been studied yet. This study was conducted by using polylactic acid (PLA), cotton, polyethylene terephthalate (PET), and poly acrylic (PAC) fibres containing yarns and their blends. Knitted fabrics, single pique, were made from these yarns by using weft knitting machine. The fabrics were washed ten times for 15 and 60 minutes under 40 °C by using conventional and ultrasonic washing methods. The main aim was to determine the effect of washing methods on the thermophysiological properties of the fabrics. It is also aimed to analyse and evaluate the thermophysiological properties of the PLA fabrics. The incorporation of 100 % PLA and cotton/PLA yarns into single pique knitted fabrics has been attempted to produce for the first time and studied their thermal comfort properties. The results show that the washing processes have a critical importance for the tested fabrics in terms of thermal conductivity, thermal resistance, thermal absorbtivity, water vapour permeability, and heat loss. It has been also demonstrated that the fabric cleaning by using ultrasonic method enhanced the properties of tested fabrics such as thermal conductivity and % recovery. It was also noted that 15 minutes ultrasonically washed fabrics had significantly lower thermal resistance as compared to conventionally washed fabrics.     

Hand and mechanical properties of stretch fabrics

This paper discusses the hand and preference of stretch fabrics for sportswear through subjective and objective hand evaluations. Twenty-two varieties of stretch fabrics for fall/winter sportswear fabrics were used. Seven main factors were classified through subjective evaluation and total cumulative variance value was 68.28 %. According to correlations between objective hand and preference, there are differences in preferences for pants and shirts, and in response to preference for shirts, no gender differences are observed. Correlations between subjective hand and preference show that there is a similar tendency in preferences for pants and shirts regardless of usage by different genders.     

Study on the dyeing properties of hemicyanine dyes. I. Acrylic fabrics

Three hemicyanine dyes were employed in dyeing acrylic fabrics following traditional cationic dyeing procedures. The influence of the substituting groups of the dye molecule on the sorption rate and sorption isotherms was analyzed. The results showed that those dyed acrylic fabrics using hemicyanine dyes had obvious fluorescent effect in the spectra range 550–750 nm. In addition, according to the EN-471 standard (2003), the chromaticity of dyed acrylic fabric was calculated to evaluate whether hemicyanine dye could meet the requirements of the fluorescent dye for high visibility warning clothing.     

Analysis and tensile-tear properties of abraded denim fabrics depending on pattern relations using statistical and artificial neural network models

The aim of this study is to develop new pattern denim fabrics and characterize the mechanical properties of these fabrics after abrasion load. Furthermore, tensile and tear strengths of these fabrics have been analysed by using the Artificial Neural Network (ANN) and statistical model. All denim fabrics were first abraded and subsequently tensile and tearing tests were applied to the abraided fabrics seperately. Actual data generated from the tests were analyzed by ANN and regression model. The regression model has shown that tensile strength properties of the abraded large structural pattern denim fabrics are generally low compared to that of the small structural pattern and traditional denim fabrics. On the other hand, when the abrasion cycles are increased tensile properties of all denim fabrics are generally decreased. Tearing strength of weft and warp in the abraded large structural pattern denim fabrics are between small structural pattern and traditional denim fabric. On the other hand, when the abrasion cycles are increased tearing strength properties in the weft and warp for all denim fabrics are generally decreased. The results from ANN and regression models were also compared with the measured values. It is concluded that almost all values from ANN are accurately predicted compared with those of the regression model. Therefore, we suggest that both methods can be used in this study as viable and reliable tools.