Multi-response robust design based on improved desirability function

Robust design, which is an important technology of continuous quality improvement activity, has been widely applied to optimal design of product or process. In this paper, a new approach integrating an improved desirability function and dual response surface models is proposed to tackle the problem of multi-response robust design. We build two desirability functions for mean and variance through combining desirability function and dual response surface models, respectively. Furthermore, we separately give objective weights for mean desirability function and variance desirability function by using entropy weight theory. Then, the overall desirability function considering location effect and dispersion effect is optimized by a hybrid genetic algorithm to obtain the optimum parameter settings. An example is illustrated to verify the effectiveness of the proposed method. The results show that the proposed approach can achieve more robust and feasible parameter settings.