Process Optimization of a Superfinishing Machine through Experimental Design and Mixed Response Surface Models

ABSTRACT

This article 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 superfinishing. 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—for example, 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 superfinishing process. Furthermore, random effects are included in the final optimization step. The contribution of this article is the study of this new superfinishing 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.

KEYWORDS:

• mixed response surface model
• robust design optimization
• manufacturing technology
• surface roughness

Additional information

Notes on contributors

Rossella Berni

Rossella Berni obtained a Ph.D. in Applied Statistics from the University of Florence in 1995. Since 1995 she has been a researcher and she is currently Associate Professor at the University of Florence, Department of Statistics, Informatics, Applications-DiSIA ”G.Parenti” where she teaches statistical quality control and design of experiments. She has been a member of the Italian Statistical Society (SIS) since 1996 and a member of the European Network for Business and Industrial Statistics (ENBIS) since 2002. She has been an IEEE member since 2012. Her current research interests include design of experiments, response surface methodology and the multiresponse case, optimal experimental designs.

Matteo Burbui

Matteo Burbui graduated in Business Administration and Quality Engineering and since 2002 has been working as an Engineer in the Advanced Technology Department of General Electric Oil & Gas.