Experimental-based optimization of polymer injection molding process parameters using anfis-ga method

Compared with ordinary injection-molded parts, the slender, cantilevered, and thin-walled plastic parts are harsh on the injection molding process conditions. For complexity and particularity, it is difficult to form such parts. It is also more likely to cause excessive warpage deformation, affecting the molding quality and performance. The automobile audio shell is a typical slender, cantilevered, thin-walled plastic part. When the mold structure and material are determined, optimizing its injection molding process is the most economical and effective method to manufacture the products with the optimum properties. In order to minimize the warpage deformation, the adaptive network based fuzzy inference system (ANFIS) and genetic algorithm (GA) were adopted to optimize the injection molding process parameters. In particular, considering the high-dimensional nonlinear relationship between the process parameters and the warpage, the ANFIS is constructed as the prediction model of the warpage. Then, the GA is used to globally optimize the prediction model to determine the optimal process parameters. The results show that the optimization method based on ANFIS-GA has a good performance. The warpage is reduced to 0.0925 mm while reduced by 88.25 %. The optimal injection molding process parameters are used for simulation and manufacture, verifying the effectiveness and reliability of the optimization method.