The Research On Optimization Of Injection Molding Process Parameters Of Plastic Gear Based On CAE

Gear is widely, commonly used and is an important force transfer or movement transfer part in mechanical, aerospace and automotive industries. The rapid development of plastic materials and injection molding technology, greatly promoting the development and application of plastic gears, plastic gear is widely used in various fields because of its unique and excellent performance. Injection molding is a complicated process,the plastic forming method is affected by many factors and has strong interaction;Furthermore for injection molding, compared to the general injection molded parts, teeth of plastic gear with complex contours which is a relatively complex structure, that makes process of injection molding of plastic gear is more complex. Among the many factors, the molding process parameters have the most direct impact on the process of injection molding of plastic gear, this article will use the injection molding CAE technology to study the effect of process parameters on the quality of molding of plastic gears and optimize the quality of plastic gear.First, study on the flow behavior of polymer melts and heat transfer in the process of injection molding combine with the melt rheology theory, and establish the mathematical model of the injection molding of plastic gear numerical simulation process.Secondly, built the 3D solid models of plastic gear on UG NX 8.0 software, and then build a CAE analysis model of injection molding of plastic gear in Moldflow 2012 by using the import STL format, providing a model for the simulation and analysis of the plastic gear.Thirdly, according to the experimental design programs, obtain the test dates of injection molding combined with CAE simulation. Obtain the degree of influence and significant of process parameters on the single quality index by using the extreme value analysis and signal-to-noise ratio analysis of variance, get the best orthogonal technology for each quality indicators.Then, build the comprehensive score of plastic gear on multi quality index based on the fuzzy mapping and weighted score. BP neural network model of plastic gear quality prediction is established by using the sample dates, proving that the model precision is a model with higher and better prediction ability by contrast with the response surface model.Finally, based on the BP neural network agent model of plastic gear forming quality, for global optimization by combining the genetic algorithm, obtain the optimal conditions.The comprehensive score is 78.8836 verified by Moldflow with round dates; improve nearly 22.68% compared to results in 64.3012 of the Moldflow recommended process. The results show that the multi objective optimization is reasonable and feasible.