PREDICTING YARN TENSILE STRENGTH USING ELMAN NETWORK

1
PREDICTING YARN TENSILE STRENGTH USING ELMAN
NETWORK

• Josphat Igadwa Mwasiagi,
• Huang XiuBao and Wang XinHou
• Department of Textile Engineering, Donghua
  University, Shanghai

2
Introduction

• The influence of fiber properties to the yarn
  strength characteristics has been a subject of
  study by many researchers, Jackowski et al,
  2002 Ureyen et al, 2006, Ghosh et al, 2005(a)
  Ghosh et al, 2005(b)
• The reported artificial Neural Networks (NN)
  models use HVI characteristics together with yarn
  fineness and TPI as inputs
• The reported models have different models for
  rotor and ringframe yarns
• This paper discusses the design of a single NN
  model to predict the tensile strength of rotor
  and ring spun yarns

3
Elman Network

• -The Elman network is a type of a recurrent feed
  forward neural network, with a feedback
  connection from the output of the hidden layer
  neurons to the input of the network,
• -The Elman network has tansig neurons in its
  hidden (recurrent) layer, and purelin neurons in
  its output layer

4
The architecture of Elman Network
5
Training Algorithm

• Training involves adjusting the weights and
  biases of the network so as to minimize the
  networks performance function
• This can be done using a by using a technique
  called backpropagation (BP), which involves
  performing computations backwards through the
  network Ham et al, 2003

6
Fletcher-Reeves Update method

• - Fletcher-Reeves Update is a modification of the
  BP technique
• -it is much faster than the original BP technique

7
Materials

• Cotton lint and yarn samples were collected from
  four textile factories in Kenya. For every yarn
  sample collected, a sample of the corresponding
  cotton lint mixture used to spin the yarn was
  also collected
• The details of the cotton and yarn samples
  collected are given in table 1. A total of 410
  samples were collected.
• The quality characteristics of the cotton lint
  and yarn samples were measured under standards
  laboratory conditions in Shanghai-China

8

Table 1 Cotton Lint and Yarn samples
Cotton Mill Machine Yarn Spinning Lint Code Type Ne Speed (rpm)
Meru AR D Rotor 27 68,0000 Meru AR D Rotor 12.5 68,0000 Meru AR D Rotor 7.5 57,0000 Voi AR B Ringframe 30 11,0000 Voi AR B Ringframe 20 10, 000 WT AR A Ringframe 30 12,000 Kitui AR A Ringframe 30 12,0000 Kitui AR A Ringframe 24 11,0000 Kitui AR C Ringframe 24 8,000
9
Methods

• A strength prediction algorithm was design
  as shown below.

10
Methods

• The NN model used Elman network with
  Fletcher-Reeves Update as the BP network training
  algorithm and gradient descent with momentum as
  the weight/bias learning function was designed.
• Several options for the number of neurons in the
  hidden layer were tried, in order to arrive at an
  optimum design
• During training, mean square error (mse), which
  is the average squared error between the networks
  and the targeted outputs, was used as the
  performance function
• To investigate the performance of the network in
  more details, a regression analysis between the
  networks response and the corresponding targets
  was performed.

11
Results and Discussions

12
Results and Discussions
13
Results and Discussions

• From figures 2 and 3 the network stabilizes at 6
  neurons. Increase of the number of neurons above
  6 does not cause any significant change in the
  performance or prediction ability of the network.
  

14
Results and Discussions

• The performance of the trained network for the
  training, validation and test subsets are given
  in figure 4. Figure 5 shows the linear regression
  between the network outputs and the corresponding
  targets.

15
Results and Discussions
16
Results and Discussions

• The final mse value for the test data was 0.0156.
• The network outputs tracks targets reasonable
  well, with a correlation coefficient (R-value) of
  0.974.

17
Conclusions

• An Elman Network model was trained using
  Fletcher-Reeves Update conjugate gradient
  training algorithm.
• The network predicted the tensile strength of
  cotton yarn samples consisting of ring and rotor
  spun yarns, giving an mse value of 0.0156. The
  correlation coefficient (R-Value) between
  predicted and targeted values for the network was
  0.974.

18
Q and A

• Thank You for Your Kind Attention