Central Composite Experimental Designs for Multiple Responses With Different Models

Central composite designs (CCDs) are widely accepted and used experimental designs for fitting second-order polynomial models in response surface methods. However, these designs are based only on the number of explanatory variables being investigated. In a multiresponse problem where prior information is available in the form of a screening experiment or previous process knowledge, investigators often know which factors will be used in the estimation of each response. This work presents an alternative design based on CCDs that allows main effects to be aliased for factors that are not related to the same response. This results in fewer required runs than current designs, saving investigators both time and money, by taking this prior information into account. R-package “DoE.multi.response” is included as a supplement for constructing these designs.