Intelligent Algorithm Optimization Study On Mobile Phone Shell Injection Molding Process

With the rapid development of the national economy has also led to the rapid development of the plastics industry and the range use of plastic products in expanding. But various problems in the injection molding process, especially when molding thin-walled plastic products, warpage and shrinkage is too large, engineers often plagued by a major problem that how to reduce, to prevent the products shaping often affect product. In the mobile phone industry, the vast majority of parts are composed of plastic products, current research focus of the plastics processing industry is plastic molding CAE technology, but there are still a range of issues in the injection molding CAE technology, especially in the forming cell phone case thin-walled plastic products.This work includes the following aspects of the content:1. This paper provides an overview of the thin-wall injection molding technology and warpage related mechanism of the thin-walled parts, as well as the basics of polymer rheology, and then analyze the factors that affect the amount of warpage and volume shrinkage, pointed out variety of measures to reduce small thin-walled parts warpage and shrinkage, thus discussed and determined some research methods of the thin-walled warpage.2. Evaluation of multiple process parameters influence on injection molding warping deformation quantity and size of shrinkage ratio. With cell phone casing parts as the research object and building simulation model, arranged experiment by taguchi test method, obtain the warpage amount and volume contraction rate of the test data by simulate and analysis the injection molding of plastic parts. Different process parameters play an essential role on the warpage and shrinkage control, at the same time we can get the minimum combinations of process parameters of the warpage.3. Based on neural network establish a nonlinear mapping relationship from the injection molding process parameters to the warpage amount and volume shrinkage, taguchi experiment dated as an artificial neural network training samples. the input is process parameters, the out put is warpage and shrinkage of the artificial neural network model, testing ANN (Artifidal, Neural Network) the accuracy of the model by inspection of samples, and be prepare for the parameter optimization and warpage and shrinkage forecast.4. Based on artificial neural network and the process parameters optimization of the taguchi experiment. we are using ANN model instead of the CAE software with the combination of the taguchi experiment method to simulate the test within the range of process parameters, get smaller warpage amount of process parameters by futher optimization. The thesis work has shown that:we can significantly shorten the time to optimize the process parameters and to increase the efficiency of the process design by combined with the taguchi experiment, the neural network and numerical simulation of the three combination for the injection molding process parameters optimization, we can obtain more accurate results than the single use of orthogonal experiments and numerical simulation methods under certain conditions of the number of numerical simulation test.This paper made several process parameters of the combined effects of analysis to the shell injection molding warpage, to avoid the one-sidedness of a separate analysis. Use taguchi experiment, the neural network and the numerical simulation method for the optimization of mobile phone shell injection molding process parameters, under certain preconditions to ensure the accuracy of analysis, improve the efficiency of the process design work, shorten production time and improve part quality.