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.     

Characterizing the tensile properties of spun yarns using bivariate normal distribution

The tensile properties of spun yarns decisively influence its performance in various mechanical processing stages. This study is primarily aimed at simultaneous analysis of two tensile properties of spun yarns namely tenacity and breaking strain, which play crucial role in determining the frequency of warping breaks. The threshold values of yarn tenacity and breaking strain required for 20’s Ne carded cotton yarn to sustain the imposed stresses and strains during warping process have been determined using a bivariate normal distribution model. This study opens up the possibility of minimizing end breakage rate in various manufacturing processes of textile industry by engineering of spun yarns devoid of potential weak spots which are responsible for breaks.     

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.     

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.     

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).     

Developing an intelligent fiber migration simulator in spun yarns using a genetic fuzzy system

The mechanical and physical properties of spun yarns and fabrics depend not only on mechanical properties of the fibers making up the yarn, but also geometrical arrangement of fibers, known as fiber migration. The main aim of this research is to introduce a new approach to predict migratory behavior of spun yarns. Achieving the objectives of this research, general physical, mechanical and structural properties of spun yarns together with existing standards were thoroughly studied. A hybrid intelligent model was developed based on a Genetic Fuzzy System (GFS) to model the relationships between migration of fibers in spun yarns and some physical and mechanical properties of spun yarns. Results indicated that the developed fuzzy expert system can be used as an intelligent simulator to predict yarn migratory parameters.     

Prediction of strength and weavability of blended spun yarns

In this paper, we report on predicting the strength of polyester/viscose spun yarns made on ring, rotor and air-jet spinning systems. A system has been developed to measure the weavability of yarns. Hamburger’s fibre bundle theory is modified to predict the strength of blended yarns from the strengths of single-fibre component yarns. The modified model predicts blended yarn strength more accurately than the original Hamburger’s model emphasizing the importance of yarn structure on blended yarn strength. The weavability of blended yarns is measured on a CTT instrument incorporating a shedding device which addresses the stresses viz., cycle extension, flex abrasion and beat up occur during weaving. The measured weavability compared well with that obtained on a commercial Sulzer Ruti Reutlingen Webtester. Yarn structure and strength and cohesion of fibres affect the strength and weavability of yarns.     

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.     

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.     

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.     

The effect of physical ageing on torque in worsted spun yarns

The intrinsic torque of freshly spun wool yarns is affected by ageing of wool roving prior to spinning as well as the storage time of the yarn after spinning. The effect of physical ageing of roving on yarn torque properties has not been observed before and this study shows that the yarn intrinsic torque increases with ageing of the roving and decreases or relaxes with the yarn storage time. The dependency of the intrinsic torque on the roving ageing time and the yarn storage time after spinning show a simple double-logarithmic shift factor of 0.42 compared with the value of 1 found generally for amorphous polymeric materials. The self-plying twist of the yarns used in this study shows a close link to the intrinsic torque and both are affected by the history of the roving prior to spinning. Significant reductions in the self-plying twist were obtained when deaged rovings were used in spinning. When self-plying twist is used as a predictor of fabric spirality the roving and yarn history needs to be considered. This study shows that low intrinsic torque yarns can be produced by deageing of the roving prior to spinning.     

Study on the static and dynamic strengths and weavability of spun yarns

This study reports on the analysis of tenacity and breaking elongation of ring-, rotor- and air-jet-polyester/viscose spun yarns measured using static- and dynamic tensile testers. The weavability, a measure of performance of these yarns in post spinning operations is quantified. The yarn diameters and helix angles of fibres in these yarns are measured in order to analyze the effect of types of spun yarn and blend proportion on yarn elongations. The dynamic tenacity is highly correlated with the weavability than the average static tenacity measured at 500 mm gauge length. The minimum static tenacity obtained from 100 tests has high correlation with the dynamic tenacity. The present study indicates that it is appropriate to evaluate the performance of spun yarns in winding, warping and weaving based on the dynamic yarn tenacity measured while running a 200 m length of yarn in a constant tension transport tester or the minimum static yarn tenacity obtained using any conventional constant rate extension (CRE) tensile testers corresponding to a total test length of 50 m.     

An investigation of methodology and apparatus to assess twist liveliness of spun yarns

A methodology and apparatus have been proposed to indirectly evaluate twist liveliness of spun yarns by measuring the number of snarl turns formed of a yarn submerged in a water bath. A comparative study was carried out to evaluate the effectiveness of water in the measurement of yarn snarls. T tests showed that water has a significant effect in the snarl forming and testing results. Systematic studies were then carried out in the intra and inter laboratories to evaluate the feasibility and accuracy of the proposed measurement system. The analysis of variance (ANOVA) for the studies showed that there is no significant difference in measuring twist liveliness between the operators in the same laboratory and between the operators from different laboratories, respectively. The largest variance in the tests is attributed to the expected variation in the level of snarl turns in different yarn specimens. The experimental results showed that the developed yarn snarling apparatus has made accurate and repeatable measurements of twist liveliness over a range of 100 % cotton ring spun yarn counts.     

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.     

Estimating regional productivity based on demographic structure with artificial neural network

This paper explores the impact of the age structure on regional productivity. An estimation model based on artificial neural network (ANN) was developed on the assumption that demographic change, due to aging and migration has a significant effect on the regional productivity, especially in rural regions. A multilayer perceptron ANN model was applied to consider the composition of demographic structure rather than ratio between two population groups such as aged-child ratio. Regional productivity was estimated by applying the estimation model developed in this research study to population and aggregate product data of sixteen South Korean cities and counties, from 2000 to 2011. Developed model is trained with data of sixteen cities and counties, from 2000 to 2009, and verified with observation data and estimation results of 2010 and 2011. The results revealed that gross regional domestic product per capita, which represents regional productivity, is significantly related to demographic structure and can be estimated by age structure.     

Development of an objective fabric pilling evaluation method. II. Fabric pilling grading using artificial neural network

An objective pilling evaluation method has been developed using image analysis and artificial neural network. Pilling parameters obtained in the previous study were used as the input values for neural network. A total of 9 data sets including 5 standard grades and 4 interpolated intermediate grades were used for training the network. Nine samples were prepared to verify the validity of the trained network in comparison with the subjective evaluation results. 18 woven and 12 knitted samples were tested to investigate the effect of the fabric structure on the performance of the network. Finally, 55 woven fabric samples were tested to evaluate the performance of the newly developed method and it was proven to be suitable for the evaluation of pilling grade especially for woven fabrics.     

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.     

Yarn engineering using hybrid artificial neural network-genetic algorithm model

This work aims to manufacture cotton yarns with requisite quality by choice of suitable raw materials for a given spinning system. To fulfill this aim, a hybrid model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been developed which captures both the high prediction power of ANN and global solution searching ability of GA. In an attempt to achieve a yarn having predefined tenacity and evenness, a constrained optimization problem is formulated with the ANN input-output relation between fibre and yarn properties. GA has been used to solve the optimization problem by searching the best combination of fibre properties that can translate into reality a yarn with the desired quality. The model is capable in identifying the set of fibre properties that gives requisite yarn quality with reasonable degree of accuracy.     

The use of k-means and artificial neural network to classify cotton lint

The use of High Volume Instrument (HVI) to measure cotton lint characteristics produces high dimensional data. A model which utilized Kohonen Self Organizing Maps (SOM) to visualize cotton lint HVI data, k-means clustering technique to cluster the data and Probabilistic Neural Network (PNN) for data classification was designed and tested using Kenyan cotton lint. According to the model the Kenyan cotton lint can be grouped into four clusters, which were successfully classified by using PNN with a correlation coefficient (R-value) of 1.