Optimization Of Injection Molding Process Parameters Based On CAE Technology

Plastic injection molding is one of the most common used molding methods. The quality of the injection moldings depends on mold design, material characteristics, set of process parameters and environment conditions.In actual production, effects of materials performance is difficult to be changed, effects of machine performance is difficult to be adjusted, effects of environmental conditions can be ignored and effects of the mold on the product quality seem to be stable after the mold processing. The process parameters of injection molding are in direct relation to the flow, molding and cooling condition of melt in the mold cavity, with the most direct impact on the quality of injection molding products. Therefore, the quality of injection molding products can be improved by changing the process parameters. Because the parameters are interactive and changing over time, their impacts on the quality of injection molding products are different. Plastic injection is non-linear and flexible multi-factors manufacturing process. By utilizing the CAE technology, the optimization of injection molding process parameters to improve the plastic products quality and to reduce the production cost has a great important meaning in the practical engineering.Taking tensile specimen, impact specimen (no gap) and impact specimen (with gap) three parts for example and warpage and shrinkage as the optimization objective, in this paper the process parameters were optimized in order to improve the plastic products quality by using Taguchi, Neural Network model, Genetic Algorithm and Moldflow.First, the three-dimensional models of the three parts were established in PRO/E and the finite element model was established in Moldflow, including the establishment of the gating system and cooling system. The established model was analyzed and the feasibility of the model was validated via simulation in Moldflow. The Moldflow recommended process parameters were obtained by the molding window analysis. By simulation analysis using the recommended process parameters, the warpage value and shrinkage rate of the plastic product was0.8036mm and1.05%, respectively. Second, the design of orthogonal experiment was discussed for the selected process parameters by Taguchi method. Mold temperature, melt temperature, injection pressure, injection time, packing pressure, packing time and cooling time were optimized by using the signal-to-noise ratio, range analysis and variance analysis methods, then optimal injection molding process parameters were determined. The results show that packing pressure and melt temperature have significant influence on warpage and shrinkage, followed by injection time and cooling time, with the other process parameters having the least effect.Third, BP Neural Network model is established to describe the relationship between process parameters and warpage and shrinkage, and the correctness and reliability of the established neural network model was verified. The warpage and shrinkage under the combination of the other process parameters were predicted by the predicted function of neural network. Using the optimal injection molding process parameters as parameters benchmark, the single factor change tests and interaction tests were conducted to study the influence of the process parameters and interaction on warpage and shrinkage. The results show that the warpage value and shrinkage rate decrease with packing pressure, melt temperature, cooling time, packing time, injection time increasing and increase with mold temperature increasing and decrease first and then increase with injection pressure increasing. Genetic Algorithm was used for finding out the global optimizing process parameters in the given range of process parameters, then optimal combination of process parameters were obtained. The warpage value and shrinkage rate of the plastic product was0.6273mm and0.8169%under the optimal combination of process parameters via prediction of Neural Network, respectively. In the practical production process, the combination of optimal process parameters shall be rounded up. By simulation analysis in Moldflow using the round combination of optimal process parameters, the warpage value and shrinkage rate of the plastic product was0.6300mm and0.82%, which decreased21.60%and21.90%from the warpage value and shrinkage rate under the recommended process parameters, respectively.Finally, the injection molding experiment was conducted by using the combination of optimal process parameters. Then the validity of the CAE simulation was verified by measuring the plastic products.