Injection Molding Warpage Optimization Based on a Mode-Pursuing Sampling Method

Abstract

This paper presents a new optimization approach for minimizing the warpage defect of injection-molded plastic parts. Existing methods in warpage optimization are either computationally expensive or, when inexpensive surrogate models are employed with fixed set of sample points, the accuracy of the surrogate model will only be ensured by a large number of sample points, which in turn will increase the amount of required computation. To address this problem, this paper applies a mode-pursuing sampling (MPS) method for warpage optimization, by integrating injection molding simulation with MPS, and by proposing a reinforced convergence criterion for the optimization process, in an attempt to search for the optimal process parameters of injection molding for minimizing warpage defect both effectively and efficiently. The MPS method can systematically generate more sample points in the neighborhood of the current optimal solution while statistically covering the entire search space. A case study of a scanner frame, where injection time, melt temperature and mold temperature are selected as the design variables, demonstrates that the proposed optimization method can effectively decrease the warpage deflection of an injection-molded part with significantly less computation required. Based on the optimization results, the paper also studied the influences of different process parameters on the severity of the warpage defect, providing a guideline for the setting of the proper process parameters.