Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network

Malik, Samander Ali; Gereke, Thomas; Farooq, Assad; Aibibu, Dilbar; Cherif, Chokri

doi:10.1080/00405000.2017.1393786

Cited by 7 publications

(9 citation statements)

References 19 publications

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“…Malek et al evaluated the performance of ANN for the prediction of yarn crimp in woven barrier fabrics [ 24 ]. They performed two experiments to predict yarn crimp.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer’s satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

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“…Malek et al evaluated the performance of ANN for the prediction of yarn crimp in woven barrier fabrics [ 24 ]. They performed two experiments to predict yarn crimp.…”

Section: Classification Based On Textile Processesmentioning

confidence: 99%

Classification of Textile Polymer Composites: Recent Trends and Challenges

2021

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“…In another study, Malik et al proposed ANN for the prediction the warp and weft yarns crimp in woven barrier fabrics. The use of ANN to predict yarn crimp has shown good results in the predicted output, especially for the warp yarn (with less prediction error between actual and predicted output) 14 . In another experimental study, Malik et al used ANN for the prediction of antimicrobial performance of chitosan/AgCl- coated fabrics.…”

Section: Introductionmentioning

confidence: 99%

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Amor

Noman

Petrů

2021

Sci Rep

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This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ($$p<0.1 e^{-10} $$ p < 0.1 e - 10 ) compared to MLR. The correlation coefficient of MLP model ($$>95\%$$ > 95 % ) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

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“…In general, textile processes are mostly non-linear in nature and a lot of efforts are applied to obtain optimal solutions 17 – 20 . ANN is an excellent approach that has been widely used for the prediction of various properties of textile materials where it has proven its effectiveness and potential, such as: prediction of the tensile properties of even and uneven yarns extracted from polyester-cotton blend 21 ; prediction of the warp and weft yarns crimp in woven barrier fabrics 22 ; prediction of antimicrobial performance of chitosan/AgCl-TiO 2 coated fabrics 23 ; prediction of core spun yarn strength, elongation and rupture 24 ; prediction of cotton fibre 25 ; prediction the change of shade of dyed knitted fabrics 26 ; prediction of coatings process on textile fabrics 27 ; and prediction of thermal resistance of wet knitted fabrics 28 . These mentioned work reveal that the most common type of ANN algorithm used in textile industry is multilayer perceptron MLP 29 , 30 .…”

Section: Introductionmentioning

confidence: 99%

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites

Amor

Noman

Petrů

et al. 2021

Sci Rep

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This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input–output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.

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Prediction of yarn crimp in PES multifilament woven barrier fabrics using artificial neural network

Cited by 7 publications

References 19 publications

Classification of Textile Polymer Composites: Recent Trends and Challenges

Classification of Textile Polymer Composites: Recent Trends and Challenges

Prediction of functional properties of nano $$\hbox {TiO}_2$$ coated cotton composites by artificial neural network

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites

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