Analysis and prediction of air permeability of woven barrier fabrics with respect to material, fabric construction and process parameters

Malik, Samander Ali; Kocaman, Recep Türkay; Kaynak, Hatice Kübra; Gereke, Thomas; Aibibu, Dilbar; Babaarslan, Osman; Cherif, Chokri

doi:10.1007/s12221-017-7241-5

Cited by 19 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above discussed literature reveal that the most common ANN type used in textile industry is MLP 30 , 31 . MLP is a class of backpropagation ANN that has the advantages of self-learning, high nonlinearity resolution and the ability of mapping between input and output variables without introducing a mathematical model between nonlinear data and precisely predict the best function.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Tadesse et al 10 and Yu et al 11 applied ANN for comfort of fabrics. ANN were also extensively conducted to evaluate fabric properties [12][13][14][15] and other aspects in yarn and fabric processes. 16,17 The above researches do help to unveil quantitative relationships between process variables and performances in varied textile problems, but how to choose the most appropriate surrogate model for an unexplored engineering problem based on incomplete information is a thorny question.…”

Section: Introductionmentioning

confidence: 99%

Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

Liu

et al. 2021

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

show abstract

“…2,3 Beside these parameters, the porosity characteristic of woven fabric also varies according to yarn crimp, warp and weft thread densities, yarn shearing, and yarn cross-section shape under the flattening effect at intersection points. 4…”

mentioning

confidence: 99%

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

Alsayed

Çelik

Kaynak

2020

Textile Research Journal

Self Cite

View full text Add to dashboard Cite

The number of filaments in yarn cross-section, weave density, and weave type are considered the most important factors that affect the property of air permeability of multifilament woven fabrics. Microfilament yarns significantly affect the air permeability property of this type of fabric because of the low porosity between the filaments. This study deals with the development of a fuzzy logic model for predicting the air permeability of multifilament polyester woven fabrics produced from conventional and microfilament yarns. The polyester multifilament yarns used in this study were produced with three different microfilament fineness and two conventional filament fineness levels. The woven fabric samples used in this study were obtained in three weave types: plain, twill, and satin, and with five different weave densities. In accordance with the experimental test results, both regression analysis and fuzzy logic system were built. The air permeability results generated from the developed fuzzy model and the regression equations were compared with the experimental values. Satisfactory and accurate prediction results were obtained with the developed fuzzy logic model. The mean absolute error of the fuzzy model and regression analysis were found to be 2.32%, 12.59%, respectively. Therefore, it was confirmed that the fuzzy model was superior in predicting air permeability.

show abstract

Analysis and prediction of air permeability of woven barrier fabrics with respect to material, fabric construction and process parameters

Cited by 19 publications

References 17 publications

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

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

Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

Contact Info

Product

Resources

About