Abstract:

Robust optimization is time-consuming for uncertainty analysis. Aiming to improve computing efficiency, a sequential robust optimization method was proposed, which combined dual-Kriging model with multi-point sampling criterion. Dual-Kriging model, constructing the relationship between variables and standard deviation of the objective, reduced the computing time of uncertainty analysis in optimization to improve computing efficiency. Multi-point sampling criterion realized sequential iteration process, and improved the global optimum. The test results of a mathematical function showed that the proposed robust optimization method was more efficient in comparison with common surrogate-based robust optimization. Lastly, the proposed optimization method was applied to optimizing the wall-thickness and injection process for a box-shape injection molded part by considering the instability of injection process, and the results showed that the optimization method was effective.