A Global Robust Optimization Using the Kriging Based Approximation Model

The current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies but it produces uncontrollable uncertainties to efficiently objectify or automate the process. To increase manageability of such uncertainties, the Taguchi method, reliability-based optimization and robust optimization are commonly being used. The main functional requirement of a mechanical system is to obtain the target performance with maximum robustness. In this research, a design procedure for global robust optimization is developed using kriging and global optimization approaches. Robustness is determined by kriging model to reduce a number of real functional calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust optimum of a surrogate model. As the postprocess, the global optimum is further refined by applying the first-order second-moment approximation method. Mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method