Combining DOE and Krigging Surrogate Modeling for Injection Molding Warpage Optimization

Warpage is one of the defects that affect both the appearance and the usage of an injection molded plastic part. The conventional simulation-based optimization method has suffered from the intensive computation due to iterative runs of CAE simulation, especially for the warpage optimization. In this paper, we propose a new method by combining a Design of Experiment (DOE) and a surrogate modeling technology, where DOE is used for determining the samples and Krigging modeling is used for developing the surrogate model to substitute the expensive CAE simulation. A global optimization algorithm is subsequently applied to optimize the warpage on the developed surrogate model. The case study of a plastic cover shows that the proposed method can be used for warpage optimization more efficiently than the pure simulation-based optimization method.