Prediction of Yarn Proerties using ANN models (Acase Study of the Kenyan Cotton Ring Spun Yarn).pdf

1、The Kenyan textile industry witnessed a rapid growth when Kenyan gained its independence in 1963 and the number of integrated textile mills (consisting of spinning, weaving and dyeing units) increased from 6 t0 52, with

2、an installed capacity of 115 million square meters, along with 110 large scale garment manufacturing units in 2 decades. This made the sub-sector the second largest manufacturing industry after food processing. Despite t

3、he many problems which have plagued the Kenyan textile industry, especially in the late 1990's the textile industry has maintained its position as one of the important industrial sectors in the Kenyan economy and also ra

4、nked among the major foreign exchange earners. The aim of this thesis was to improve the prediction efficiency of yarn quality properties prediction models with special reference to the Kenyan manufactured ring spun cott

5、on yam.
　　 In order to achieve the above stated aim this thesis has been divided into six chapters. Chapter one deals with literature review, short comings of the previous research work the general outline and contri

6、butions of this thesis. The literature review covered the types of mathematical, empirical, statistical and artificial neural network (ANN) models used to study the cotton ring spun fiber-to-yam process. The conclusion o

7、f the literature review indicated that in comparison with statistical models the ANN models give better performance. On the other hand the statistical model has an advantage over mathematical and empirical models during

8、the study of the fiber to yarn process. Therefore it can be concluded that the ANN models perform better than statistical, mathematical and empirical models during the study of the complex multi-stage fiber-to-yarn proce

9、ss. In view of the above mentioned conclusions, yam quality properties prediction models were designed using ANN models.
　　 From the literature review undertaken in chapter one three shortcomings of the previous rese

10、arch work were identified. These short comings included network generalization, network scale and efficiency of the network training algorithm. Network generalization (R-value), which is defined as the ability of the net

11、work to respond to unseen data is critical forth application of the network in solving day to day problems in the industry. This area has received little attention especially for the fiber-to-yarn process. The second are

12、a of immense importance to the performance of the ANN models is the scale of the model. Most researchers use many input factors for the purpose of obtaining a low network error. While the use of more input factors may le

13、ad to a more comprehensive study of the problem, there’s however the danger of increasing the scale. More input factors normally give a lower network error but leads to difficulties in training due to the increased scale

14、. On the other hand fewer input factors give less training problems due to the reduced scale but the network error may be slightly higher. Therefore there is a need for a study of the scale reduction methods for yarn qua

15、lity properties prediction models. The third short coming which this thesis will address is the improvement of the network training algorithms. While there are many training algorithms which can be used to train ANN mode