The Optimization of Plastic Injection Molding Process Based on Support Vector Machine and Genetic Algorithm

The paper presents the radial basis kernel parameters of the support vector machine (SVM) regression model employed to determine the complex and nonlinear relationships between the injection molding parameters and the defects of plastic injection molded parts, whereas genetic algorithm (GA) is applied to determine a set of optimal nuclear parameters for SVM. Then, an approximate analysis model is established, and it is proved effective by numerical examples of the plastic injection molded parts. All these explored an effective method of numerical simulation model for optimization of the plastic injection molding process.