Warpage optimization of a bus ceiling lamp base using neural network model and genetic algorithm

In this study, optimum values of process parameters in injection molding of a bus ceiling lamp base to achieve minimum warpage are determined. Mold temperature, melt temperature, packing pressure, packing pressure time and cooling time are considered as process parameters. In finding optimum values, advantages of finite element software MoldFlow, statistical design of experiments, artificial neural network and genetic algorithm are exploited. Finite element analyses are conducted for combination of process parameters designed using statistical three-level full factorial experimental design. A predictive model for warpage is created using feed forward artificial neural network exploiting finite element analysis results. Neural network model is validated for predictive capability and then interfaced with an effective genetic algorithm to find the optimum process parameter values. Upon optimization, it is seen that genetic algorithm reduces the warpage of the initial model of the bus ceiling lamp base by 46.5%.