Research On Process Optimization Method For Plastic Injection Molding Based On Artificial Neural Network And Genetic Algorithm

In this study, a hybrid optimization method for optimizing the process parameters during plastic injection molding was proposed. This proposed method combines a BP neural network method with an intelligence global optimization algorithm, i.e. genetic algorithm (GA). A multi-objective optimization model was established to optimize the process parameters during plastic injection molding on the basis of the finite element simulation software Moldflow, Orthogonal experiment method, BP neural network as well as Genetic algorithm. Optimization goals and design variables (process parameters during plastic injection molding) are specified by the requirement of manufacture. A BP artificial neural network model is developed to obtain the mathematical relationship between the optimization goals and process parameters. Genetic algorithm is applied to optimize the process parameters that would result in optimal solution of the optimization goals.A case study of a plastic article is presented. Warpage as well as clamp force during plastic injection molding are investigated as the optimization objectives. Mold temperature, melt temperature, packing pressure, packing pressure time and cooling time are considered to be the design variables. The case study demonstrates that the proposed optimization method can adjust the process parameters accurately and effectively to satisfy the demand of real manufacture.In addition, a kind of automobile glove compartment cap was utilized in this paper. A BP neural network model for warpage prediction and optimization of injected plastic parts has been developed based on key process variables including mold temperature, melt temperature, packing pressure, packing time and cooling time during PIM. Trained by the results of FE simulations conducted by orthogonal experimental design method, the BP neural network based prediction system got the mathematical equation mapping the relationship between the process parameter values and warpage value of the plastic. It has been proved that the prediction system has the ability to predict the warpage of the plastic within an error range of2%. Process parameters have been optimized by using the genetic algorithm. The optimized warpage value is0.804mm, which is shortened by66%comparing to the initial warpage result2.358mm. The final product can satisfy with the matching requirements and fit the automobile glove compartment well. Research results further confirmed the validity and reliability of the proposed BP-GA based optimization method for plastic injection forming.