An approach to optimize correlated multiple responses using principal component analysis and desirability function

While researchers have developed several approaches to attain design variable settings that simultaneously optimize multiple-quality characteristics, the multi-response optimization has become a common practice in complicated manufacturing processes. Most of these research works assume independency of responses where their variances are constant over the experimental space. However, there are many manufacturing processes in practice where the quality characteristics under consideration are correlated. In this study, an efficient approach based on principal component analysis and a conventional desirability function is proposed to optimize correlated multiple responses. This approach not only obtains optimal operating conditions, but also considers different variance and correlation levels of responses and enforces all objectives to satisfy constraints. Experimental results obtained using a standard example show the effectiveness of the proposed method.