| Summary: | 碩士 === 國立臺灣科技大學 === 纖維及高分子工程研究所 === 87 === In this paper, the quality specificity about the cotton and the Open-End (OE) Rotor Spun yarn is studied. First, the Taguchi method is applied for experiment design to find the fittest cotton collocation. Second, the cotton specificity is measured by using High Volume Instrument system and the suitable orthogonal array is applied to design the experimental plan. Then, the Rieter OE Rotor Spinning Machine M 1/1 is used for spinning the OE Rotor yarn. Finally, the Uster Tester-3 is employed to measure the specificity of yarn.
For quality consideration, the intelligent control is applied for factorial analysis. After training and testing the back-propagation neural network, the mean square error can be converged to 0.1. Then, the genetic algorithm is employed to find the optimal solution, and the best of corresponding solution in the cotton specificity conditions can be found. In this study, the predictive model of the correlation about characteristics of OE yarn and cotton is proposed. In other words, the condition of OE yarn characteristics can be obtained under the fitness condition of cotton collocation.
The predictive model of the correlation about characteristics of cotton and OE yarn is implemented by the evolve perceptron neural network. It combined with genetic algorithm and the back-propagation neural network. It has the better predictive accuracy and the faster converge velocity than that use of the back-propagation neural network.
The predictive model is supported by the experiment. The average error can be converged to 11%. It is proved that the model can predict the condition of OE yarn characteristics effectively and exactly. At the same time, the condition of OE yarn characteristics can also be obtained under the fitness condition of cotton collocation.
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