Artificial Neural Networks: Applications to Textiles

Chattopadhyay, R.; Guha, Anirban

doi:10.1080/00405160408688961

Cited by 63 publications

(33 citation statements)

References 61 publications

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“…In general, ANN is a parallel interconnected structure consisting of: input layer of neuron (input variables), a number of hidden layers, and output layer (response or responses). According to Kolmogorov's theorem, ANN with a single hidden layer should be capable of approximating any function to any degree of accuracy (Chattopadhyay & Guha, 2004).…”

Section: Ann Methodologymentioning

confidence: 99%

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Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns

Dadgar

Varkiyani

Merati

2014

The Journal of The Textile Institute

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In the present paper, a response surface model has been introduced to predict the geometrical parameters of heat set polypropylene pile yarns. The input factors of the presented model include yarn twist, initial yarn count, time, and temperature of heat setting and the response factors are yarn count, yarn shrinkage, crimp contraction and packing factor after the heat setting process. To analyse the effect of this process on the yarn parameters, the dry heat setting process has been applied to all samples at different times and temperatures using an oven equipped with air circulation because of better accuracy and control of temperature. The obtained results showed that there is a positive relation between time and temperature and output parameters. Finally, the predicting equations discussions about the optimum points for maximum shrinkage and interactions of parameters have been presented. Hence, due to some disability of the RSM method, an ANN model has been designed to predict the parameters at higher accuracy. The results of the accomplished ANN model represent a higher prediction correlation coefficient compared to RSM.

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Section: Ann Methodologymentioning

confidence: 99%

“…Thus, multi-layer perceptron ANN with one hidden layer was utilized according to Kolmogorov's theorem. For all data-sets, the hyperbolic tangent sigmoid transfer function (Equation (14)) in the hidden layer and a linear transfer function (Equation (15)) in the output node was employed (Chattopadhyay & Guha, 2004).…”

Section: Ann Modeling and Analyzingmentioning

confidence: 99%

Comparison between artificial neural network and response surface methodology in the prediction of the parameters of heat set polypropylene yarns

Dadgar

Varkiyani

Merati

2014

The Journal of The Textile Institute

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“…But, Chattopadhyay and Guha [4] show in their study that the increase of the hidden layers number is able to produce a considerable improvement of performances of the network if only all the other parameters of the network are optimised.…”

Section: Measurement Of the Hanging Weightmentioning

confidence: 99%

Prediction of Drape Coefficient by Artificial Neural Network

Ghith

Hamdi

Fayala

2015

Autex Research Journal

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An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

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“…There are several research papers focus on the efficiency of the Computer Aided Design (CAD) tools in the textile industry (see, for instance (Bingham and Hague, 2013) and (Chattopadhyay and Guha, 2004)). This efficiency can be determined by the decrease of the production time of a same collection regarding the traditional method; the reduction of physical prototype tests; the reliability (the capacity of decision grew); the flexibility (the capacity rectification becomes agile); the creativity: accentuating the ability to manifest the creativity of designers, related to the concept of unevenness (Green, 1989), deviation of ideas in the process of creation; and the organization process: including organization reinforcements of the products creating processes that allow the transmission or correct information in each state of creation (suitable formats and presentations).…”

Section: Cbr and The Textile Industrymentioning

confidence: 99%