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Optimization Study on Gating System and Molding Process Parameters of Injection Mold Based on Simula

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Tutor: WangChuanYang
School: Suzhou University
Course: Mechanical Manufacturing and Automation
Keywords: Injection mold,Gating system,Molding process parameters,BP Neural Network,Geneti
CLC: TQ320.662
Type: Master's thesis
Year:  2011
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Plastic Injection molding is one of the most common used molding methods. With the development of the industry and the wide use of injection molding technology, Plastic products are being used in almost every trade of life. Thus manufacturers are paying more and more attention to the quality of plastic products. Since warpage is one of the most important factors which affect the quality of plastic products, by minimizing the amount of warpage, the quality of plastic products can be guaranteed. To achieve this, the adjusting of the gating system and molding process parameters is needed. So, optimization of gating system and molding process parameters has practical value in engineeringThis paper, based on considerable quantities Experiment data and numerical simulation methods, used Moldflow, Orthogonal Experiment Method, Artificial Neural Network and Genetic Algorithm, aimed to research on the improvement of the quality of plastic products.First, according to the principle of establishing the gating system, different gating systems of frame part are established. Moldflow is then used to do optimum analysis on different molding schemes with different gating systems. Simulation results obtained from different molding scheme is compared in order to get the optimal molding scheme. Consequently the gating system of the frame part is optimized.Second,molding process parameters which affect the warpage of plastic part are studied. The molding process parameters which need to be studied are established according to the Orthogonal Experiment Method and Variance Analysis. The molding process parameters include mold temperature, melt temperature, packing pressure and pacing time. Each molding process parameters has four levels. Orthogonal Experiment Table L16(44) is used to analyze the experiment of four factors with four levels. The optimal combination of molding process parameters from the arranged experiments is obtained according to analyze experiment data. The affection on warpage is described.In order to find the optimal combination of molding process parameters from the global range, BP neural network and Genetic Algorithm is used in this study. Full combination experiments of four factors with four levels are arranged to establish the reliable BP neural network. BP neural network is improved on structure and algorithm. BP neural network is trained and tested by the training sample and testing sample from the experiment data. BP neural network is proved reliable from the testing result. The predicting capability of BP neural network is used to predict the warpage of frame part in other different molding process parameters. The predicting results tell the affection on warpage, caused by interaction of different molding process parameters and single molding process parameters.Lastly, based on Genetic Algorithm¡¯s capability of searching optimal solution in the global range, the actual model and experiment results are combined to establish the parameters in Genetic Algorithm. Meanwhile with the help of BP neural network, the optimal combination of molding process parameters is obtained. The comparison between final optimal results and experiment date proves the accuracy of Genetic Algorithm.This study shows that the Optimization of gating system and molding process parameters do affect the warpage of frame part. The warpage of the frame part is minimized according to the optimal results. The quality of frame part is improved. The optimal method can not only minimize the warpage, but reduce the cost of production as well, thus competitiveness can be greatly improved.
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