neural networks in fabric engineering

7/31/2019 Neural Networks in Fabric Engineering

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NEURAL NETWORKS IN FABRICNEURAL NETWORKS IN FABRIC

ENGINEERINGENGINEERING

BYBY

S.VIGNESHWARANS.VIGNESHWARAN



CONTENTS

• Engineering of knitted fabrics

• Introduction about ANN

• Types of neural networks and network selection

• Engineering of single jersey fabrics

– Network creation

– Network parameters

– Training of network

– Network simulation

– Test set

• Conclusion



What is ANN?

• An artificial neural network (ANN), usually

called "neural network" (NN), is computational

model that simulates the structure and/or unc ona aspec s o o og ca neura ne wor s

• It consists of an interconnected group of

artificial neurons and processes information

using a connectionist approach to computation.



Comparision with biological networks

HUMAN NEURON ARTIFICIAL NEURON



Application of ANN

• Prediction (input-output)

• Pattern recognition

• Classification

• Curve fitting



Types of networks



Types of network

• (1) Historically, the earliest ANNs are The Perceptron,

proposed by the psychologist Frank Rosenblatt

• (2) The Artron (Statistical Switch-based ANN) to R. Lee

• (3) The Adaline (Adaptive Linear Neuron), due to B.

Widrow, 1960). This artificial neuron is also known as the

ALC (adaptive linear combiner).

• (4) The Madaline (Many Adaline), This is an ANN

(network) formulation based on the Adaline above.



Cont..

• Three later fundamental networks are:

• (5) The Back-Propagation network A multi-layer Perceptron-

based ANN, giving an elegant solution to hidden-layers learning

• (6) The Hopfield Network. This network is different from the ear er our s n many mpor an aspec s, espec a y n s

recurrent feature of feedback between neurons.

• (7) The Counter-Propagation Network where Kohonen's Self-

Organizing Mapping (SOM) is utilized to facilitate unsupervised

learning (absence of a teacher").



Engineering of single jersey fabric

• For the following example back propagation type

network is used.

• Input vectors and the corresponding output vectors are

used to train a network until it can approximate a

function, associate input vectors with specific output

vectors, or classify input vectors in an appropriate way

as defined by you.

• Properly-trained back propagation networks tend to

give reasonable answers even when presented with

inputs that they have never seen



Training set

count (tex) Loop length in cm Finish GSM Wales/cm

30.92 0.293 230 14.1

30.92 0.32 207 12.78

20.22 0.265 160 14.71

20.22 0.303 142 12.87

20.22 0.314 135 12.41

17.68 0.253 147 16.21

17.68 0.262 138 15.65

15.03 0.231 140 18.63

15.03 0.269 122 15.97

15.03 0.249 130 17.3



Architecture of back propagation network used



Network parameters

• Training function used: trainlm

• Transfer functions used

Tansig – hidden layer

Purelin – out ut la er

• Number of neurons in hidden layer – 4

• Performance function – mse(mean square error)

• If the last layer of a multilayer network has sigmoid

neurons, then the outputs of the network are limited to a

small range.



Screen shot of training



R² = 0.956

12

13

14

15

16

17

Series1

Linear (Series1)

COUNT(Ne) LOOP LENGTH

REAL TIME VALUESOF WPC

PREDICTED VALUESOF WPC

24 0.32 12.8 10.6

30 0.28 14.6 12.7

40 0.26 15.6 13.1

10

11

10 11 12 13 14 15 16 17



Conclusion

• The supply chain of textile sector involves prediction of

the end properties for a given input at most of its

facilities(yarn & fabric).

• ANN widen the scope of forecast being flexible to any

application.

• Since quality of product has become the important

factor of survival in the globalised textile market, such

an effective forecasting tool will play a key role to

achieve the targets in a better way at various

applications of the industry.



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neural networks in fabric engineering

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