Research On The Rule Of Shrinkage Of Large Cpue Based On Neural Network

Casting polyurethane elastomer (CPUE ) is commonly called as liquid rubber. It can keep the elasticity, fine wearability and good mechanical intensity with very high hardness. It also have such characteristics as chemical stability , health adaptability and blood compatibility ,etc, and it can be made to the products with complicated structure for its good mobility. So it is widely used in military, physic and engineering.However, its shrinkage is big, and varies a lot, especially for large(above 1000mm ) polyurethane elastomer. So it is significant to the mold design of CPUE to analyze the causation for that the CPUE products shrink and to study the qualitative and quantitative relations between shrinkage and every factor.In order to analyze the effect of every factor on the shrinkage in characteristics,this article researches the effects of every factor on the shrinkage of large CPUE products on the basis of experiments. It gives the trend of the effect of every factor on the shrinkage,and analyzes the causation in detail. It points out that product structure and technical conditions have great effect on the shrinkage, and the variety of environment factors causes the subsequent fluctuation of the shrinkage. The curve in that the shrinkage varies with time is provided also. This article researches the effect of the length of flowing path on shrinkage, too.Because not considering the shape of products, the position of injection gate and the relation of every factor, orthogonal experiments can not used to predict the shrinkage quantificationally. So this article presents a method to predict the shrinkage based on artificial neural network. It makes full use of the advantage of artificial neural network to realize the quantitative prediction of the shrinkage.In order to make artificial neural network learn about product structure and predict the shrinkage of compound products, a structure model is put forward as an input parameter of artificial neural network. After learning, it can express the rule for the changes of product structure.In the end, by comparing the result of artificial neural network with the data of experiments, it is proved feasible on the shrinkage prediction.