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.     

Artificial neural network approach for predicting colour properties of laser-treated denim fabrics

In this study, artificial neural network (ANN) and linear regression (LR) approaches are proposed for predicting colour properties of laser-treated denim fabrics. Denim fabrics were treated under different combinations of laser processing parameters, including pixel time (μs), resolution (dot per inch) and grayscale (lightness percentage) as inputs. Colour properties, including colour yield (K/S sum value), CIE L*, a* and b* values and yellowness index were predicted as outputs in these approaches. Later, the prediction performances of two approaches were compared and the statistical findings revealed that ANN approach was able to provide more accurate prediction than LR approach, especially for L value. Moreover, among the three input variables, grayscale (lightness percentage) was found to be the most important factor affecting colour properties of laser-treated denim fabrics.     

Modeling spun yarns migratory properties using artificial neural network

The mechanical and physical properties of spun yarns and fabrics depend not only on properties of constituent fibers, but also the yarn structure characterized by geometrical arrangement of fibers in the yarn body. Although there are many studies related to analyzing the migratory properties of spun yarns, there are no studies available about predicting yarn migration parameters. Therefore, the main aim of this research is to introduce a new approach to predict migratory properties of different kinds of spun yarns, namely siro, solo, compact and conventional ring-spun yarns. To achieve the objectives of the research, general physical and mechanical properties of spun yarns together with existing standards were thoroughly studied. Spun yarn migratory properties were predicted using intelligent technique of artificial neural network (ANN). Results signified that the ANN models can predict precisely the yarn migratory properties on the basis of a series of yarn physical and mechanical properties.     

Dimensional and mechanical characterization of newly developed denim fabrics based on experimentally determined property-structural pattern relations for upholstery applications

The aim of this study was to develop new pattern denim fabrics and characterize the dimensional, the abrasion and pilling properties of these fabrics. Furthermore, tensile and tear strengths of these fabrics were determined. The potential enduses of pattern denim fabrics were evaluated by comparing the test results with traditional denim fabrics. The produced fabrics were classified as ‘Design group I’ and ‘Design group II’. In design group I, the fabrics had small structural patterns whereas the structural patterns of the fabrics of design group II were large. The dimensional properties and weights of developed pattern denim fabrics in both of the design groups were different in terms of weft densities, structural pattern sizes which influenced the numbers, directions and distributions of warp-weft interlacement. The abrasion behaviours of the traditional denim fabrics and the fabrics with large-small structural patterns were similar. However, it was determined that the fabrics with large and small patterns were abraded on the earlier abrasion cycles compared to the traditional denim fabrics. The pilling resistances of the fabrics not only depended on the hairiness levels of the yarns used during weaving, but also on the pattern sizes of the fabrics. The small structural pattern fabrics showed more resistance to pilling than those of the large structural pattern fabrics. There was a decrease on the warp and weft tensile strengths of the large structural pattern fabrics in comparison with the traditional denim fabrics. The average tear strengths of the large structural pattern denim fabrics on the warp course were higher than those of the traditional denim fabrics while the tear strengths of the large pattern and traditional denim fabrics on the weft course were similar to each other. The end-uses of the newly developed structural pattern denim fabrics were recommended as home textile.     

Predicting the unevenness of polyester/viscose blended open-end rotor spun yarns using artificial neural network and statistical models

In this study, an artificial neural network (ANN) and a statistical model are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Seven different blend ratios of polyester/viscose slivers are produced and these slivers are manufactured with four different rotor speed and four different yarn counts in rotor spinning machine. A back propagation multi layer perceptron (MLP) network and a mixture process crossed regression model (simplex lattice design) with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed) are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Both ANN and simplex lattice design have given satisfactory predictions, however, the predictions of statistical models gave more reliable results than ANN.     

Predicting bending rigidity of woven fabrics using artificial neural networks

This paper reports an investigation on the predictability of bending property of woven fabrics from their constructional parameters using artificial neural network (ANN) approach. Number of cotton grey fabrics made of plain and satin weave designs were desized, scoured, and relaxed. The fabrics were then conditioned and tested for bending properties. Thread density in fabric, yarn linear density, twist in yarn, and weave design were accounted as input parameters for the model whereas bending rigidity in warp and weft directions of fabric formed the outputs. Gradient descent with momentum and an adaptive learning rate back-propagation was employed as learning algorithm to train the network. A sensitivity analysis was carried out to study the robustness of the model.     

An artificial neural network approach to prediction of the colorimetric values of the stripped cotton fabrics

This paper presents an artificial neural network (ANN) modeling by Levenberg-Marquardt (LM) algorithm for predicting the colorimetric values of the stripped cotton fabrics dyed using commercial reactive dyes. Achieving the expected efficiency in the application of stripping process is a very important aspect for the success of the reproduction. In the study, the predictions of L* and ΔE colorimetric values of stripped cotton samples for different stripping applications by artificial neural network are reported. We set up different network structures with different number of nodes in the hidden layer, the number of inputs and MSE of results as stopping criteria in order to get the best fitting model. According to the result of the best neural network models predicting L* and ΔE, we achieved 97 % of R for both of them. We are able to predict the L* value of the stripped samples using some working parameters as inputs with only 1.2 % error. We think that our results are very promising and the predictions of L* and ΔE values of stripped samples before applying any process are possible using the ANN model set up in the study, especially for L*.     

Prediction of the changes on the CIELab values of fabric after chemical finishing using artificial neural network and linear regression models

Changes on the CIELab values of the dyed materials after the different chemical finishing treatments using artificial neural network (ANN) and linear regression (LR) models have been predicted. The whole structural properties of fabrics and some process data which were from fiber to the finishing parameters were accepted as inputs in these models. The networks having different structures were established, and it was also focus on the parameters which could affect the performance of the established networks. It was determined that we could successfully predict the color differences values occurring on the material after the finishing applications. In addition, we realized that some ANN parameters affected the prediction performance while establishing the models. After training ANN models, the prediction of the color difference values was also tried by linear regression models. Then, extra ANN models were established for all outputs using the parameters as inputs in the LR equations, and the prediction performances of both established models were compared. According to the results, the neural network model gives a more accurate prediction performance than the LR models.     

Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties

In this study artificial neural network (ANN) models have been designed to predict the ring cotton yarn properties from the fiber properties measured on HVI (high volume instrument) system and the performance of ANN models have been compared with our previous statistical models based on regression analysis. Yarn count, twist and roving properties were selected as input variables as they give significant influence on yarn properties. In experimental part, a total of 180 cotton ring spun yarns were produced using 15 different blends. The four yarn counts and three twist multipliers were chosen within the range of Ne 20–35 and α _e 3.8–4.6 respectively. After measuring yarn tenacity and breaking elongation, evaluations of data were performed by using ANN. Afterwards, sensitivity analysis results and coefficient of multiple determination (R²) values of ANN and regression models were compared. Our results show that ANN is more powerful tool than the regression models.     

Assessment of gastric cancer survival: using an artificial hierarchical neural network

This study is designed to assess the application of neural networks in comparison to the Kaplan-Meier and Cox proportional hazards model in the survival analysis. Three hundred thirty gastric cancer patients admitted to and surgically treated were assessed and their post-surgical survival was determined. The observed baseline survival was determined with the three methods of Kaplan-Meier product limit estimator, Cox and the neural network and results were compared. Then the binary independent variables were entered into the model. Data were randomly divided into two groups of 165 each to test the models and assess the reproducibility. The Chi-square test and the multiple logistic model were used to ensure the groups were similar and the data was divided randomly. To compare subgroups, we used the log-rank test. In the next step, the probability of survival in different periods was computed based on the training group data using the Cox proportional hazards and a neural network and estimating Cox coefficient values and neural network weights (with 3 nodes in hidden layer). Results were used for predictions in the test group data and these predictions were compared using the Kaplan-Meier product limit estimator as the gold standard. Friedman and Kruskal-Wallis tests were used for comparisons as well. All statistical analyses were performed using SPSS version 11.5, Matlab version 7.2, Statistica version 6.0 and S_PLUS 2000. The significance level was considered 5% (alpha = 0.05). The three methods used showed no significance difference in base survival probabilities. Overall, there was no significant difference among the survival probabilities or the trend of changes in survival probabilities calculated with the three methods, but the 4 year (48th month) and 4.5 year (54th month) survival rates were significantly different with Cox compared to standard and estimated probabilities in the neural network (p < 0.05). Kaplan-Meier and Cox showed almost similar results for the baseline survival probabilities, but results with the neural network were different: higher probabilities up to the 4th year, then comparable with the other two methods. Estimates from Cox proportional hazards and the neural network with three nodes in hidden layer were compared with the estimate from the Kaplan-Meier estimator as the gold standard. Neither comparison showed statistically significant differences. The standard error ratio of the two estimate groups by Cox and the neural network to Kaplan-Meier were no significant differences, it indicated that the neural network was more accurate. Although we do not suggest neural network methods to estimate the baseline survival probability, it seems these models is more accurately estimated as compared with the Cox proportional hazards, especially with today's advanced computer sciences that allow complex calculations. These methods are preferable because they lack the limitations of conventional models and obviate the need for unnecessary assumptions including those related to the proportionality of hazards and linearity.     

Predicting the influence of seam design on formability and strength of nonwoven structures using artificial neural network

Formability which is also known as drapability is defined as the ability of a planar textile structure to be directly deformed to fit a three-dimensional surface without the formation of wrinkling, kinks or tears. According to human’s desire for comfortable and high quality clothing, formability has a specific place in the textile industry so many studies have been conducted on understanding and predicting formability of textiles. Artificial neural network method is used in this study order to predict the influence of seam design on formability and tensile behavior of nonwoven structures. Our findings and analysis showed that seam design, seam allowance and weight of nonwoven layers are three main parameters significantly affecting the formability and overall tensile of nonwoven structure. Predicted values obtained from the ANN methodology were compared with the experimental data proving very good correlation between examined and predicted values.     

Using artificial neural network to predict colour properties of laser-treated 100% cotton fabric

In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics.     

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.     

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.     

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.     

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.     

Prediction of CIELab data and wash fastness of nylon 6,6 using artificial neural network and linear regression model

We tried to predict the CIELab data and wash fastness values of scoured nylon 6.6 knitted fabric dyed with 1:2 metal-complex acid dyes and aftertreated using three different methods named as syntan, syntan/cation and full backtan by artificial neural network (ANN) with Levenberg-Marquardt algorithm and regression models. Afterward, the predicting performance of these models was tested and compared with each other using unseen data sets. We were able to achieve to predict the all colorimetric data satisfactorily such as L*, a*, b*, C, h ^o and wash fastness performance using both models. The statistical findings indicated that the regression models provide more accurate prediction for all colour data with an average error of 1% contrast to previous study. In terms of prediction of fastness, artificial neural network is a bit more useful than regression models for prediction of staining value on the nylon part of adjacent multifiber.     

Modeling waterbird diversity in irrigation ponds of Taoyuan, Taiwan using an artificial neural network approach

The study develops an approach adopted by artificial neural networks (ANN) to model the relationship between pondscape and waterbird diversity. Study areas with thousands of irrigation ponds are unique geographic features from the original functions of irrigation converted to waterbird refuges. The model considers pond shape and size, neighboring farmlands, and constructed areas in calculating parameters pertaining to the interactive influences on avian diversity, among them the Shannon–Wiener diversity index. Results indicate that irrigation ponds adjacent to farmland benefited waterbird diversity. On the other hand, urban development leads to the reduction of pond numbers, which reduces waterbird diversity. By running the ANN model, the resulting index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r < 0.28).     

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.