A warpage optimization method for injection molding using artificial neural network with parametric sampling evaluation strategy

A sequential optimization design method based on artificial neural network (ANN) surrogate model with parametric sampling evaluation (PSE) strategy is proposed in this paper. The quality index, such as warpage deformations, thickness uniformity, and so on, is a nonlinear, implicit function of the process conditions, which are typically evaluated by the solution of finite element (FE) equations, a complicated task which often involves huge computational effort. The ANN model can build an approximate function relationship between the design variables and quality index, replacing the expensive FE reanalysis of the quality index in the optimization. Moldflow Corporation's Plastics Insight software is used to analyze the quality index of the injection-molded parts. The optimization process is performed by a Parametric Sampling Evaluation (PSE) function. PSE is an infilling sampling criterion. Although the design of experiment size is small, this criterion can take the relatively unexpected space into consideration to improve the accuracy of the ANN model and quickly tend to the global optimization solution in the design space. As examples, a scanner, a TV cover, and a plastic lens are investigated. The results show that the sequential optimization method based on PSE sampling criterion can converge faster and effectively approach to the global optimization solution.