| Gas-assisted injection molding (GAIM) technology, being an innovative injection molding technology, uses compressed gas to create hollowed voids in the plastic part. Compared with conventional injection molding, there are some new gas-related processing parameters, among which, the pressure of injected gas plays the most important role. To significantly improve the quality of product, it requires the accurate monitoring and control of gas injection pressure.The principle and the equipment of gas injection molding are outlined first, and then the gas pressure control system (GPCS) is elaborated. Thus a mathematical model of GPCS that can describe the gas pressure during the process is derived; furthermore, the stability and controllability based on this model are analyzed. To improve the dynamic performance and steady-state performance, a controller using Ackerman state feedback approach is designed. This paper applies some advanced control methods, such as fuzzy control and fuzzy neural network control to the control of gas injection pressure. The advantages, disadvantages and algorithms of the two control methods are discussed.Simulations are carried out by computer program written in Simulink, a toolbox of Matlab. The performances of the three control methods mentioned above on the control of gas pressure under three-segment reference signal are compared. The results demonstrate that fuzzy neural network control performs best in GAIM process.At last, a method of how to design the experimental software for GPCS is presented in this thesis. And a friendly man-machine interface designed by Visual C++ shows good operation of this system in project.
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