A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage

Abstract

This paper presents a hybrid optimization method for minimizing the warpage of injection molded plastic parts. This proposed method combines a mode-pursuing sampling (MPS) method with a conventional global optimization algorithm, i.e. genetic algorithm, to search for the optimal injection molding process parameters. During optimization, Kriging surrogate modeling strategy is also exploited to substitute the computationally intensive Computer-Aided Engineering (CAE) simulation of injection molding process. With the application of genetic algorithm, the “likelihood-global optimums” are identified; and the MPS method generates and chooses new sample points in the neighborhood of the current “likelihood-global optimums”. By integrating the two algorithms, a new sampling guidance function is proposed, which can divert the search process towards the relatively unexplored region resulting in less likelihood of being trapped at the local minima. A case study of a food tray plastic part is presented, with the injection time, mold temperature, melt temperature and packing pressure selected as the design variables. This case study demonstrates that the proposed optimization method can effectively reduce the warpage in a computationally efficient manner.