Optimization of injection molding process parameters based on RBF-NSGA algorithm
To solve the injection molded problem of sink marks and warpage,the optimization of the injection molding process was studied by the bucket of trash as an example.The samples of process data and quality data are build by Moldflow analysis and orthogonal experimental design.The radial basis function(RBF)model for the data is build by Isight software,the accuracy of the model is verified.Injection molding process parameters are optimized by non-dominant sorting genetic algorithm Ⅱ(NSGA-Ⅱ)genetic algorithm,the process data after algorithm optimization is verified by simulation.The result shows the error between the output of the algorithm and the simulation result is very small,the maximum sink index error is 1.94％,and the warpage deformation erroris 1.27％.The optimized simulation maximum sink index is 1.449％,compared with the recommended process of Moldflow,it is reduced by 64.8％.The optimized simulation maximum warpage deformation is 7.882 mm,compared with the recommended process of Moldflow,it is reduced by 23.48％.The test sample looks good and the size meets the assembly requirements.The experimental results are in agreement with the analytical results,which shows the method can guide production.
injection molding process simulationmoldflowneural network modelgenetic algorithmmulti-objective optimization
万方数据
