Process optimization of a superfinishing machine through experimental design and mixed response surface models

This paper deals with process optimization for a centrifugal compressor. More precisely, the technological problem concerns the reduction of the surface roughness of centrifugal compressor impellers through a new technology implemented by GE Oil & Gas, called super-finishing. The new technology is studied through statistical methods in order to achieve a minimization of the final roughness according to the best set of levels for the abrasive component mixture and the time process. To this end, an experimental design is planned for three different materials, e.g. three types of steel, and mixed response surface models are applied. The application of mixed models allows us to estimate random effects, useful for better controlling the process variance in a robust design approach. Within this framework, a random effect is the initial roughness, measured for each impeller vane before starting the super-finishing process. Furthermore, random effects are also included in the final optimization step. The contribution of this paper is the study of this new super-finishing process through mixed response surface models and robust design optimization, in order to set the best levels of the abrasive component mixture and time process to minimize the final roughness for a centrifugal compressor impeller.