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Optimization of Process Parameters for Thin Plastic Shell Injection Molding

Author: ChenXiaoPing
Tutor: HuShuGen；SongXiaoWen
School: Zhejiang University
Course: Vehicle Engineering
Keywords: Injection molding Numerical Simulation Warpage Optimization of process parameters Orthogonal test Neural Networks
CLC: TQ320.5
Type: Master's thesis
Year: 2005
Downloads: 524
Quote: 22
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Abstract


Injection molding is the preferred method of mass production of plastic products. But how to reduce, prevent the products shaping frequent warping phenomenon, is a major problem that often plagued engineers. And rely solely on traditional experience, technical know-how, and constantly trying, still can not solve the problem effectively. Therefore, CAE technology used in injection molding, warpage prediction to optimize the injection molding process parameters set to improve production efficiency and forming quality, new ideas to solve the problems of warpage. This work mainly includes the following aspects: 1. Comprehensive survey and evaluation of the impact of multiple process parameters of injection molding warpage amount. Generally thin plastic shell as the research object to build simulation models by orthogonal test arrangements experiments, experimental analysis of numerical simulation of the injection molding of plastic parts to obtain the amount of warpage test data. The extent to study the effects of different process parameters on the injection molding process warpage, concluded: injection time process parameters such as injection temperature, dwell time parameter control also played an essential role in the amount of warping. and minimal warpage combinations of process parameters. Established from the injection molding process parameters based on neural network nonlinear relationship to the amount of warpage. The data obtained by using orthogonal test as neural network training samples to obtain input process parameters, the output of the neural network model of the amount of warpage, and tested the ANN (Artificial NeuralNetwork) the accuracy of the model by testing samples for parameter optimization and warpage prediction prepare. Optimization of process parameters based on neural network and orthogonal experiment. Within the range of process parameters, the ANN model instead of the CAE software numerical simulation test, combined with orthogonal experiment method further optimize the process parameters such smaller amount of warpage. Thesis work: neural network and orthogonal experiment and numerical simulation of the three used in the injection molding process parameter optimization can significantly shorten the time of the optimization of process parameters, process design efficiency, both the numerical simulation of the number of trials under certain conditions, can be obtained than simply using the more accurate the results of orthogonal experiments and numerical simulation methods. 4. ANN application in the amount of warpage prediction. Nonlinear mapping relationship between the use of neural networks in the range of the training samples can be given a set of process parameters a warpage, without having to re-use the CAE software to develop time-saving process. The features of this work is that of a thin shell injection warping deformation made the combined effects of several process parameters to avoid a separate analysis of the one-sidedness of various factors, so that the depth of warpage in injection molded parts; neural network technology and orthogonal test method, numerical simulation combined for the optimization of the injection molding process parameters guarantee under the premise of the analysis accuracy, significantly saving time process developed to improve the efficiency of the process design.

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CLC: > Industrial Technology > Chemical Industry > Synthetic resins and plastics industry > General issues > Machinery and equipment
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