Injection Molding Process Optimization And Product Weight Control

So many ways of plastic processing, including injection processing, extrusion processing, compression injection ect, had been used in plastic processing of industry. Injection molding is one of the most important polymer-processing technology used to making complex and precial productions, and it is as high efficiency and high production way for plastic molding. Injection molding process has a complication physical process, high temperature melt was putted into cool-wall with high pressure, the melt temperature declined rapidly, Melt was frozen, shaped and crystallized. Volume has shrinked. The quality of injection-molded part is deeply relative to material properties, mold design, plastic processing parameters setup, and environment condition in injection molding process. Process parameters has direct effects on the melt behavior in the cavity and the final part quality, so it is important to get the optimization processing parameters for the improvement of the part quality.In this paper, the investigation on plastic part quality control and process optimization in injection molding process is mainly carried out. On the base of numerical simulation, we optimization and control of injection molding process were implemented using Matlab and Simulink. The achievements included:1. Orthogonal array experiments based on Taguchi DOE were conducted to investigate the relationship of part weight with processing parameters. The relative important process variables and the optimal factor level combination were obtained, the analysis of an industry part indicated that Injection time holding pressure and mold temperature had relative influence on part weigh.2. Regression equation between the process variables and weight of molding part was built. The model was achieved using MATLAB. Based on the results, comparison of the results of regression molded and numeral results was satisfied.3. According to the theory of automation control, theories and algorithms of fuzzy control was investigated and built. The simulation of fuzzy PID control was achieved using Matlab and Simulink. Applications of PID control and fuzzy PID control to an industry part indicate the fuzzy PID control has more steady property and less error.