A stepwise optimization approach based on Gaussian process (GP) surrogate model is proposed to determine the process parameters and improve the quality control for injection molding. In order to improve the global performance in this optimization, an enhanced probability of improvement criterion is also introduced. Firstly, GP surrogate model is constructed with the initial samples which are obtained from an optimal design of experiment method. GP is capable of giving both a prediction and an estimate of the confidence for the prediction simultaneously. Secondly, an enhanced probability of improvement criterion is used to find the direction of adding training samples and optimize the surrogate model. Since the global optimal region of the model become accurate efficiently after steps of optimizing the surrogate model, the proposed enhanced probability of improvement criterion can switch more swiftly to global optima compared with other improvement criterion. Finally, an auto front grille molding process is taken as an example to illustrate the method. The results show that the proposed optimization method can effectively decrease the warpage of injection-molded parts.

In this paper, an adaptive optimization method based on Gaussian process (GP) surrogate model is proposed to minimize the warpage of injection molding parts. GP surrogate model combining design of experiment (DOE) methods is used to build an approximate function relationship between warpage and process parameters, replacing the expensive simulation analysis in the optimization iterations. First, establish an approximation function of the relationship between warpage and process parameters by a small size of design of experiment with GP surrogate model. And then, an enhanced probability improvement criterion is used to determine how additional training samples could be added to optimize the surrogate model. Comparing with expected improvement criterion, proposed enhanced probability improvement criterion can switch to global optima more swiftly. Finally, a front grille molding processing is taken as an example to illustrate the criterion. The results show that the proposed optimization method can effectively decrease the warpage of injection molding parts.

In engineering applications, Gaussian process (GP) regression method is a new statistical optimization approach, to which more and more attention is paid. It does not need pre-assuming a specified model and just requires a small amount of initial training samples. Based on the design of experiment (DOE), determining a reasonable statistical sample space is an important part for training the GP surrogate model. In this paper, a novel intelligent method of DOE, the translational propagation algorithm, is employed to obtain optimal Latin hypercube designs (TPLHDs). It also proved that TPLHDs' performance is superior to other LHDs' optimization techniques in low to medium dimensions. Using this method, the best settings of the process parameters are determined to train GP surrogate model in the injection process. A automobile door handle is taken as an example, and experimental results show that the proposed TPLHD performs much better than the normal LHD in the quality of fitting GP surrogate model, so taking TPLHDs instead of LHDs' optimization technique for training GP model is practical and promising.