Research On Optimization Of Injection Molding Process Parameters Based On CAE

At present,injection molding is widely used in plastic industry.For many complex products,it can be formed at one time,with twice the result with half the effort.However,the process of injection molding is complex and changeable,and there are many factors affecting the quality of products,such as the attributes of materials themselves,factory environment and molding parameters.In the actual production process,the production materials are usually specific and cannot be changed;the impact of the environment can also be neglected,and the injection parameters have the most significant impact on the various states of the melt,including flow,die and cooling,which has a direct impact on the quality of injection molded parts.Therefore,in order to improve the quality of plastic parts,it is of great significance to optimize the process parameters of injection molding by using CAE technology for actual processing and production.Taking a plastic impeller as the research object,taking warpage deformation,shrinkage index and volume shrinkage as quality indexes,using Taguchi method,BP neural network,genetic algorithm and grey relational analysis method,Moldflow 2016 to analyze and optimize injection process parameters,in order to improve the quality of injection products.(1)First,create a 3D solid model(CAD model)of the part in UG,then export the part in IGES format,then import it into Moldflow software to generate the mesh,and obtain the 3D CAE analysis model through mesh repair.The lattice matching rate is 93.2%,which fully meets the analysis requirements.Then the gating system and the cooling system are further established.Through the simulation analysis of the injection molding,the feasibility of the model is verified and the preparation for the next step is well prepared.(2)Secondly,the six factors that have a great influence on product quality are design variables.The quality evaluation indexes of the products in this paper are volume shrinkage,warpage deformation and shrinkage index.Twenty-five sets of experiments were designed by Taguchi method.The experimental data were simulated by Moldflow software.The effects of various process parameters on volume shrinkage,warpage deformation and shrinkage index were studied.(3)Then,based on the simulation experimental data,the BP inversion prediction model of the volumetric shrinkage of the plastic impeller is established on the MATLAB platform based on the above six influencing factors and the volume shrinkage rate.The prediction error control of the model is established.Within 5%,the prediction effect is more accurate.Genetic algorithm was used to optimize the parameters and the optimum process scheme was obtained.(4)Finally,aiming at minimizing the three evaluation indicators proposed above,a more effective multi-objective decision optimization method is proposed.Based on the principal component analysis method to calculate the influence weight of the factors on the gray correlation degree,a multi-objective optimization model of the plastic impeller injection molding process parameters was established to find the best process plan.