Predicting the tensile strength of polyester/cotton blended woven fabrics using feed forward back propagation artificial neural networks

Tensile strength plays a vital role in determining the mechanical behavior of woven fabrics. In this study, two artificial neural networks have been designed to predict the warp and weft wise tensile strength of polyester cotton blended fabrics. Various process and material related parameters have been considered for selection of vital few input parameters that significantly affect fabric tensile strength. A total of 270 fabric samples are woven with varying constructions. Application of nonlinear modeling technique and appreciable volume of data sets for training, testing and validating both prediction models resulted in best fitting of data and minimization of prediction error. Sensitivity analysis has been carried out for both models to determine the contribution percentage of input parameters and evaluating the most impacting variable on fabric strength.     

Optimization of the needle punching process for the nonwoven fabrics with multiple quality characteristics by grey-based taguchi method

This study is intended for finding out the optimal processing parameters for needle punching nonwoven fabrics in order to work out its maximal strength. Taguchi method together with grey relational analysis is employed to resolve the problem as regards multiple-quality optimization, and further discover the optimal combination of processing parameters for needle punching nonwoven fabrics. Firstly, orthogonal array L₁₈(2¹×3⁷) is used to deal with the processing parameters that may exert influence over the manufacturing of needle punching nonwoven fabrics. Then grey relational analysis is applied to resolve the deficiency of Taguchi method that focus on single quality characteristic. Next, the response table of grey relational analysis is used to obtain the optimal combination of processing parameters for multiple quality characteristics. In the current experiment quality characteristic refers to the tensile strength and tear strength of the nonwoven fabrics. Additionally, signal-to-noise ratio (SN ratio) calculation and analysis of variance (ANOVA) can be adopted to explore the experimental results. Through ANOVA, the significant factors that exert comparatively significant influence over the quality characteristic of the needle punching nonwoven fabrics, that is, the control factors are determined so that the quality characteristic of the needle punching nonwoven fabrics can be effectively controlled. Finally, confirmation experiment is conducted within 95 % confidence interval to verify the experimental reliability and reproducibility.     

Empirical modelling of tensile strength of woven fabrics

Aesthetic properties of fabrics have been considered as the most important fabric attribute for years. However, recently there has been a paradigm shift in the domain of textile material applications and consequently more emphasis is now being given on the mechanical and functional properties of fabrics rather than its aesthetic appeal. Moreover, in certain woven fabrics used for technical applications, strength is a decisive quality parameter. In this work, tensile strength of plain woven fabrics has been predicted by using two empirical modelling methods namely artificial neural network (ANN) and linear regression. Warp yarn strength, warp yarn elongation, ends per inch (EPI), picks per inch (PPI) and weft count (Ne) were used as input parameters. Both the models were able to predict the fabric strength with reasonably good precision although ANN model demonstrated higher prediction accuracy and generalization ability than the regression model. The warp yarn strength and EPI were found to be the two most significant factors influencing fabric strength in warp direction.     

Mechanical properties and crystallization behavior of polypropylene non-woven fabrics reinforced with POSS and SiO2 nanoparticles

PP/POSS and PP/SiO₂ composite non-woven fabrics filled with polyhedral oligomeric silsesquioxanes (POSS) and SiO₂ respectively using a convenient blending method were prepared through melt-blown process with corona charging. The morphology of the composite fibers and the distribution of POSS and SiO₂ nanoparticles in PP matrix were investigated by field-emission scanning electron microscope (FSEM) and transmission electron microscope (TEM), respectively. POSS and SiO₂ can act as nucleating agent and accelerate the crystallization process during nonisothermal cooling. The shear storage modulus G??, loss modulus G??, and complex viscosity ??* of non-woven fabric reduce when 1 wt % POSS was added and increase for PP5/POSS composite non-woven fabric compared with pure PP non-woven fabrics. However, all G??, G?? and ??* of PP/SiO₂ non-woven fabric decrease with increasing SiO₂ content owing to plasticization by SiO₂. Both stress and elongation at break of the PP/POSS melt-blown non-woven fabrics are improved compared with PP non-woven fabrics, however decrease when SiO₂ was added, as compared to the neat PP non-woven fabric. The onset temperature of decomposition for both the PP/POSS and PP/SiO₂ composite non-woven fabrics is higher (5?C10 °C) than pure PP and char content is increased with increasing POSS and SiO₂.     

A study of physical modification on grey cotton by laser irradiation

The surface morphology of the CO₂ laser treated grey cotton fabrics was studied which showed a characteristics sponge-like structure on cotton fibres after treating with CO₂ laser irradiation. The laser treatment parameters ranging from 100 to 150 pixel time and 40 to 70 dot per inch (dpi) were irradiated on the grey cotton fabrics directly and the degree of physical modifications, such as surface morphology, wettability and fabric strength, were changed accordingly with various laser treatment parameters. The surface morphology, wettability and tensile strength of cotton fibre treating with laser were evaluated using different instruments, such as Scanning Electron Microscope (SEM), contact angle meter and tensile strength machine. In spite of creating a sponge-like structure on fibre surface after treating with laser, the wettability of the samples was highly improved but the tensile strength was decreased.     

Selection of yarn for the predefined tensile strength of cotton woven fabrics

In order to meet the required strength of a fabric, selection of yarn is difficult because tensile strength of woven fabric depends upon a number of factors. Still, the manufacturers have to use hit and trial method in order to select the yarn for the required tensile strength of fabric. This study was carried out to develop regression equations for the prediction of yarn tensile strength suitable for the predefined strength of cotton woven fabrics. These equations were developed by using empirical data obtained from two hundred and thirty four fabric samples prepared under a systematic plan with different constructions. Prediction proficiency and precision of these regression equations were evaluated by correlation analysis of the predicted and actual warp and weft yarn strength values of another set of thirty six fabric samples. The results show a very strong prediction precision of the equations.     

Study on anisotropic creep behavior of nonwoven geotextiles

The anisotropy in creep behavior of two types of nonwoven fabrics (needle-punched and thermobonded spun laid) has been studied. It has been observed that the amount of time dependent extension depends on the direction, amount of loading and the structure of nonwoven the fabrics. The time dependent extension (creep) for the nonwoven fabric increases with the increase in amount of load. The higher initial extension and creep are observed for needle-punched nonwoven fabric as compared to thermobonded spun-laid nonwoven fabric. The creep behavior of needle-punched nonwoven shows a logarithmic relationship with time, but the thermobonded spun-laid nonwoven fabric does not show such logarithmic relationship. For a particular fabric, the creep is dependent on the fiber arrangement and is minimum in the direction in which the proportion of fiber is maximum and visa versa.     

Investigation and modeling of air permeability of Cotton/Polyester blended double layer interlock knitted fabrics

The aim of this study was to analyze and model the effect of knitting parameters on the air permeability of Cotton/Polyester double layer interlock knitted fabrics. Fabric samples of areal densities ranging from 315–488 g/m² were knitted using yarns of three different cotton/polyester blends, each of two different linear densities by systematically varying knitting loop lengths for achieving different cover factors. It was found that by changing the polyester content in the inner and outer fabric layer from 52 to 65 % in the double layer knitted fabric did not have statistically significant effect on the fabric air permeability. Air permeability sharply increased with increase in knitting loop length owing to decrease in fabric areal density. Decrease in yarn linear density (tex) resulted in increase in air permeability due to decrease in areal density as well as the fabric thickness. It was concluded that response surface regression modeling could adequately model the effect of knitting parameters on the double layer knitted fabric air permeability. The model was validated by unseen data set and it was found that the actual and predicted values were in good agreement with each other with less than 10 % absolute error. Sensitivity analysis was also performed to find out the relative contribution of each input parameter on the air permeability of the double layer interlock knitted fabrics.     

Analysis and tensile characterization of air-entangled textured polyester woven fabrics depending on interlacement and yarn sets

The aim of this study was to understand the failure mechanism of two dimensional dry fabric structure considering yarn sets and interlacements. For this purpose, data generated on air-entangled textured polyester woven fabric under the simple tensile load and analyzed by developed regression model. The regression model showed that warp and weft directional tensile strengths of satin fabric were higher than those of plain and rib fabrics in unravel sample. This might be related to the number of interlacements of the fabrics. There was not a considerable difference between warp directional tensile strength of ravel and unravel satin fabrics, whereas weft directional tensile strength of ravel satin fabric decreased rapidly with respect to its unravel form. The satin fabric showed the highest warp directional tensile strength among the others. The lowest weft directional tensile strength was received from ribs fabric. In semi-ravel sample, all fabrics showed low warp and weft directional tensile strength values except in plain fabric. Warp directional tensile elongation of plain fabric was the highest in unravel sample. Satin fabric showed the highest warp directional tensile elongation in the ravel sample. Warp directional tensile elongations of all the fabrics in the semi-ravel sample became low. Weft directional tensile elongation of satin fabric was the highest in unravel sample. In addition, satin and plain fabrics showed the highest weft directional tensile elongations in the ravel sample. Weft directional tensile elongations of all the fabrics in the semi-ravel sample became low except in ribs fabric.     

Ballistic-resistant stainless steel mesh compound nonwoven fabric

The energy of impact must decay and be transmitted after a bullet is shot through a ballistic-resistant cloth with a laminate structure. A rigid net structure transmits the impact stress to reduce the breakage of the material in the direction perpendicular to the fabric after the impacting of a projectile. This work combines the rigid net structure of stainless steel mesh with two layers a needle-punched polyamide nonwoven fabric to create a sandwich-like laminate structure. A compound fabric that is composed of a stainless steel mesh and polyamide nonwoven fabrics is placed in multi-layer Kevlar fabrics, and the buffer effect is measured by performing a dropping weight impact test and a bullet-shooting test. The specifications of the stainless steel mesh and the order of placement of the compound fabrics are varied to show the effect of these parameters on the energy of fracture propagation and the buffer effect of the multi-layered Kevlar compound fabric that includes a layer of compound fabric that is made of stainless steel mesh and polyamide nonwoven fabrics. In this study, the compound fabric replaces several layers of Kevlar unidirectional fabric, to be used to reduce the cost of bulletproof vests without reducing ballistic resistance.     

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.     

Studies on compression behaviour of polypropylene needle punched nonwoven fabrics under wet condition

The present study deals with the effect of parallel-laid and cross-laid web of polypropylene needle punched nonwoven fabrics on compression properties (initial thickness, percentage compression, percentage thickness loss and percentage compression resilience) under wet condition. These compression properties of polypropylene needle-punched nonwoven under wet condition have also been compared with its dry condition. With the increase in needling density the initial thickness, percentage compression and percentage thickness loss of the fabrics under wet condition decrease to higher extent compared to its dry condition both in case of parallel-laid and cross-laid fabrics. Cross-laid nonwoven fabric presents lower value of initial thickness percentage compression and thickness loss compared to parallel-laid fabric which is very prominent at high needling density (350 punches/cm²). The percentage compression resilience shows increasing trend with the increase in needling density both under dry and wet conditions of parallel-laid web. It also follows similar trend in case of cross-laid nonwoven under wet condition. The optimum needling density for compression resilience of cross-laid nonwoven fabric under dry condition is 250 punches/cm².     

An investigation in structural parameters of needle-punched nonwoven fabrics on their thermal insulation property

The thermal characteristics of hollow polyester fibers were compared with solid polyester fibers in order to study their processing behavior and performance characteristics. The effects of different processing and structural properties including fiber diameter, bulk density of layer, and surface pressure on layers of needle-punched nonwoven fabrics with hollow fibers on thermal resistance properties were also investigated. The results show that hollow fibers have a higher thermal resistance in comparison with solid ones. This is a consequence of air trapping inside the fibers, higher bulkiness, and higher surface area of hollow fibers. Furthermore, thermal resistance of microfibers is better than those of macrofibers in both hollow and solid fibers. The thermal resistance of nonwoven subjected to this study, have an inverted-U-shaped pattern versus the bulk density of the fabric. The results also showed that thermal resistance of needle-punched nonwoven fabrics can be affected by the range of heater temperature during the test, however considerably can be affected by fabric thickness as a main structural property of nonwoven fabrics.     

Optimization of the processing conditions and prediction of the quality for dyeing nylon and lycra blended fabrics

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.     

Mechanical properties of denim fabric reinforced poly(lactic acid)

Denim, a twilled cotton fabric, was used to enhance the mechanical and thermal properties of poly(lactic acid) (PLA). The denim fabric reinforced composites with different numbers of denim layers were fabricated by using a hand layup method. The impact, tensile, and dynamic mechanical properties of the composites were observed with increasing denim layers to examine the reinforcing effect of denim fabrics. Numerical analysis was carried out to model the elastic modulus of the composite by using a commercial software. Three-dimensional geometry of the denim fabric reinforced PLA composite was generated through a CAD program, and the elastic modulus was calculated by applying uniform deformation on one surface. The impact strength, tensile strength, and thermal properties of the composites were improved by piling denim fabrics. The denim fabric reinforced composites exhibited outstanding impact strength due to the retarded crack propagation as well as large energy dissipation. The 3 layer denim reinforced composite showed best results among all specimens, and its impact strength, tensile strength, and tensile modulus were measured to be 82 J/m, 75.76 MPa, and 4.65 GPa, respectively. The PLA/denim composites have good mechanical properties and can substitute traditional composites such as glass fiber or carbon fiber reinforced composites.     

Sound absorbent,flame retardant warp knitting spacer fabrics: Manufacturing techniques and characterization evaluations

This study proposes a combination for reciprocal reinforcement between warp knitting spacer fabrics and PU foams. PET/Kevlar nonwoven fabrics are made with an 80:20 ratio and an incorporation of various needle-punching speed of 100, 150, 200, 250, and 300 needles/min. Ascribing to having an optimal bursting strength, sound absorption coefficient, and limited oxygen index (LOI), the PET/Kevlar nonwoven fabric that is made by 200 needles/min are selected to be combined with a glass-fiber fabric by applying needle punch in order to form a surface layer. Next, warp knitting spacer fabrics and the nonwoven fabrics are laminated, followed by being combined with polyurethane (PU) foam that are featured with different densities of 200, 210, 220, 230, and 240 kg/m³ in order to form spacer fabric/PU foam composites with multiple functions. The composites are then tested with a drop-weight test, a compression test, a bursting strength test, a sound absorption test, and a horizontal burning test. The test results indicate that all spacer fabric/PU foam composites reach a horizontal burning level of HF1, and their sound absorption coefficients at 2500-4000 Hz also suggest a satisfactory sound absorption. In particular, the optimal residual stress and compressive strength are present when the composites contain 210 kg/m³ PU foam. Similarly, the optimal bursting strength of the composites occurs when they are composed of 230 kg/m³ PU foam. The spacer fabric/PU foam composites are proven to have high strengths, sound absorption, and fire retardant, and thus have promising potentials for use as construction materials and light weight composite planks.     

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.     

Fabrication of electromagnetic shielding polyester fabrics with carboxymethyl chitosan-palladium complexes activation

Electromagnetic shielding polyester fabrics were prepared using carboxymethyl chitosan-palladium (CMCS-Pd) complexes as activation solution, followed by electroless nickel plating. CMCS-Pd complexes were prepared by the complexing adsorption between CMCS and Pd²⁺. The effects of reaction time and pH value on the adsorption of Pd²⁺ by CMCS were discussed. The maximum adsorption capacity was calculated as 4.27 mmol/g. CMCS-Pd complexes were characterized by ultraviolet (UV) spectrophotometer and Fourier transform-infrared (FTIR) spectroscopy. The induction time of electroless plating decreased gradually with the increase of Pd²⁺ concentration. The lowest surface resistance 125 mΩ/sq of the treated polyester fabric was obtained when Pd²⁺ concentration in CMCS-Pd complex was 1.5 g/l. The prepared polyester fabrics had excellent electromagnetic shielding effectiveness (SE) of 40–60 dB. The treated polyester fabrics were also characterized by scanning electron microscopy (SEM). Results showed that CMCS-Pd was effective to form an active catalyzed layer on polyester substrate and the 1.5 g/l Pd²⁺ was sufficient to initiate electroless nickel plating reaction. The CMCS-Pd complex activation and electroless nickel plating treatment caused small changes in the polyester fabrics’ tensile strength and air permeability.     

Predicting tensile strength of yarns required for producing PET/Cotton blended woven fabrics of a pre-defined tensile strength

This study was aimed at developing statistical models for the prediction of tensile strength of warp and weft yarns required for attaining a pre-defined strength of PET/Cotton blended woven fabrics. The models were developed based on the empirical data obtained from carefully developed 234 fabric samples with different constructions using 15, 20, and 25 tex yarns in warp and weft directions. The prediction ability and accuracy of the developed models were assessed by correlation analyses of the predicted and actual warp and weft yarn strength values of another set of 36 fabric samples. The analyses showed a very strong ability and accuracy of the developed statistical prediction models.     

Analysis and off-axis tensile characterization of air-entangled textured polyester woven fabrics depending on unit cell interlacing frequency

The aim of this study is to analyze and determine the off-axis tensile properties of air-entangled textured polyester fabrics based on unit cell interlacing frequency. For this purpose, continuous filament polyester air-entangled textured yarn was used to produce plain, ribs and satin woven fabrics. The fabrics were cut from the warp direction (0°) to weft direction (90°) at every 15° increment, and tensile tests were applied to those of the off-axis samples. The strength and elongation results were introduced to the statistical model developed, and regression analyses were carried out. Hence, the effects of off-axis loading and interlacement on the directional tensile properties of the fabric were investigated. The regression model showed that off-axis loading influences fabric tensile strength. On the other hand, interlacement frequency is the most important factor for fabric tensile elongation. The results from the regression model were compared with the measured values. This study confirmed that the method used in this study as can be a viable and reliable tool. Future research will concentrate on multiaxially directional fabric and the probability that it will result in homogeneous in-plane fabric properties.