Optimization of woven fabric parameters for ultraviolet radiation protection and comfort using artificial neural network and genetic algorithm

Majumdar, Abhijit; Das, Apurba; Hatua, Piyali; Ghosh, Anindya

doi:10.1007/s00521-015-2025-6

Cited by 20 publications

(6 citation statements)

References 14 publications

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“…The application of metaheuristic algorithms (particle swarm optimizer, genetic algorithm, crow search optimizer) for accuracy improvement of ANN has gained considerable attention. Genetic algorithm (GA) was used with ANN to improve pilling performance 26 , yarn tenacity 27 , ultraviolet protection factor (UPF) 28 . Grey wolf optimizer (GWO) was combined with ANN to detect and predict the coating thickness by hyperspectral images 29 .…”

Section: Introductionmentioning

confidence: 99%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

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Section: Introductionmentioning

confidence: 99%

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor

Noman

Petrů

et al. 2022

Sci Rep

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“…The experimental results showed that ANN gave better prediction results for dimensional change. Abhijit et al applied a combination of GA and ANN as a hybrid algorithm to predict the comfort performance and the range of ultraviolet protection factor (UPF) [ 65 ]. ANN was applied as a prediction tool and GA was utilised as an optimization tool.…”

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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“…However, it is a time-consuming process 35 . Abhijit et al applied a combination of GA and ANN as a hybrid algorithm to predict comfort performance and the range of ultraviolet protection factor (UPF) 36 . ANN was applied as a prediction tool and GA was utilized as an optimization tool and for experimental purpose, a set of four samples were selected for the evaluation of functional properties.…”

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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Optimization of woven fabric parameters for ultraviolet radiation protection and comfort using artificial neural network and genetic algorithm

Cited by 20 publications

References 14 publications

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Classification of Textile Polymer Composites: Recent Trends and Challenges

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

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