Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

This article presents an integrated, simulation-based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn-around time and adequate accuracy for substituting time-consuming computer simulations during system-level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively.