Research On Simulation Analysis And Optimization Method Of Iniection Molding Products Warpage Based On The Gaussian Process

Injection molding is the most important method for producing plastic products, researchers have made many efforts to improve the quality and reduce the production cost in recent decades. The warping deformation is the key factor of plastic products’ quality, which is of quite complex mechanism and highly nonlinear to the processing factors such as product shape and processing parameters. In this study, CAE (Computer Aided Engineering) and orthogonal design were utilized to investigate warping deformation, then a Gaussian process prediction model considering different injection molding parameters was built. At last, the optimal injection molding parameters were obtained by the optimization of the model.1) Injection molding process and CAE were introduced, then the current research on warping deformation was summarized. Subsequently, the current research on warpage optimization method was summarized.2) A numerical model was built and AMI (Autodesk Moldflow Insight) was used to investigate the influences of injection time, holding pressure, holding time and cooling time etc. on the warping deformation, it was argued that uneven contraction is the critical factor of the warping deformation.3) Orthogonal design and CAE were used to investigate the effects of processing parameters such as mold temperature, melt temperature, injection time, holding pressure, holding time and cooling time etc. on the warpage. The influences of various processing conditions on the product quality were analyzed based on range analysis and variance analysis, thus the proper processing parameters were determined.4) A Gaussian process prediction model for calculation the warpage related to mold temperature, melt temperature, injection time, holding pressure, holding time and cooling time was built. Simulation results were utilized to train the model and determine the parameters. Therefore, the warpage can be estimated by this model and the optimal solution was obtained by genetic algorithm. 5) A warping deformation experimental platform was built and the warping deformations at10groups of processing parameters were measured. The results confirmed the validity of the finite element and Gaussian process prediction model.