Simulation And Parameter Optimization Of Injection Molding Process Of Light Guide Support For Automobile Instrument

With the continuous development of plastic types,the application of plastic parts in the automotive industry is becoming more and more common.For plastic parts with high precision and quality requirements,it is necessary to study the factors affecting precision and quality.The most influential and difficult to control factors are molding process parameters.Therefore,on the basis of CAE technology,improving the precision and quality of parts by changing the molding process parameters has important guiding significance for actual production.In this dissertation,the car instrument light guide bracket is used as the research object,the molding process parameters are used as the test factors,the volume shrinkage and the amount of warpage are the quality evaluation indicators.The Taguchi orthogonal test method,gray correlation analysis,and improved particle swarm optimization are used to perform the process parameters to optimize,use Moldflow to simulate and study the optimized process parameters and minimum quality evaluation indicators was obtained for injection molding of automobile instrument light guide brackets.The main contents of this dissertation are as follows:(1)Using Creo to carry out three-dimensional modeling of the car instrument light guide bracket and design the injection mold of the car instrument light guide bracket.The CAE technology was used to carry out numerical simulation analysis on the forming process of the car instrument light guide bracket,and process the data of the forming simulation results.(2)Adopting Taguchi orthogonal test design,five molding process parameters of mold temperature,melt temperature,cooling time,holding pressure and holding time were selected as test factors,and volume shrinkage and warpage were used as quality evaluation indicators.Through Moldflow simulation of the test data,the simulation simulation results were analyzed by range analysis and variance analysis to obtain the optimal volume shrinkage molding process parameter combination and the optimal warpage molding process parameter combination.The gray correlation analysis is then used to process the test results,and the combination of the molding process parameters with better volume shrinkage and warpage is obtained.(3)BP neural network was used to establish the mathematical model between the molding process parameters and the quality evaluation indicators.Taking the established model as the fitness function of the improved particle swarm optimization algorithm,using the algorithm to optimize the molding process parameters,the optimal process parameter combination was obtained.The main conclusions and innovations of this article are as follows:(1)Improve the inertia weights and learning factors in the particle swarm optimization algorithm to form a new particle swarm optimization algorithm.The simulation results show that the improved algorithm has better optimization effect.(2)Using the Taguchi orthogonal test method,gray correlation analysis and improved particle swarm optimization algorithm,the molding process parameters are optimized to obtain a set of optimal molding process parameter combinations.