Modeling, Control and Industry
This study presents a hybrid of artificial neural network and NSGA-IIfor multi-objective optimization of plastic injection molding process. The objectivesto be optimized areadimension of thefinished plastic product(product quality), processing time(productivity), and energy consumption(manufacturing cost). Thedata collection and resultsvalidation ismade in a 330 ton plastic injection machine. The design variables considered are mold temperature,material temperature, injection time, packingpressure, packingpressure time, and cooling time. Artificial neural network isused to map the relationship between design variables and output variables. Then,NSGA-IIis 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 accordingto current design requirements in terms of quality, productivityand energysaving.
- Alejandro Alvarado-Iniesta