Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method

Injection molding is the most widely used process in manufacturing plastic products. Since the quality of injection molded plastic parts are mostly influenced by process conditions, how to determine the optimum process conditions becomes the key to improving the part quality. In this paper, a combining artificial neural network and genetic algorithm (ANN/GA) method is proposed to optimize the injection molding process. In this method, a BP neural network model is developed to map the complex non-linear relationship between process conditions and quality indexes of the injection molded parts, and a GA is used in the process conditions optimization with the fitness function based on an ANN model. The combining ANN/GA method is used in the process optimization for an industrial part in order to improve the quality index of the volumetric shrinkage variation in the part. The results show that the combining ANN/GA method is an effective tool for the process optimization of injection molding.