Multi-objective Optimization of an Injection Molding Process

This study presents a hybrid of artificial neural network and NSGA-II for multi-objective optimization of a particular plastic injection molding process. The objectives to be optimized are: a dimension of the finished plastic product (product quality), the processing time (productivity), and the energy consumption (manufacturing cost). The data collection and results validation are made on a 330 ton plastic injection machine. The design variables considered are mold temperature, material temperature, injection time, packing pressure, packing pressure time, and cooling time. An artificial neural network is used to map the relationship between design variables and output variables. Then, NSGA-II is used to find the set of Pareto optimal solutions. The results show that the methodology gives the designer flexibility and robustness to choose different scenarios according to current design requirements in terms of quality, productivity and energy savings.