Robust Design of Measurement Systems

Abstract

An integrated approach for estimation and reduction of measurement variation (and its components) through a single parameter design experiment is developed. Systems with a linear signal-response relationship are considered. The noise factors are classified into a few distinct categories based on their impact on the measurement system. A random coefficients model that accounts for the effect of control factors and each category of noise factors on the signal-response relationship is proposed. A suitable performance measure is developed using this general model, and conditions under which it reduces to the usual dynamic signal-to-noise ratio are discussed. Two different data analysis strategies—response function modeling and performance measure modeling—for modeling and optimization are proposed and compared. The effectiveness of the proposed method is demonstrated with a simulation study and Taguchi’s drive-shaft experiment.

Keywords:  :

• Measurement error
• Parameter design
• Random coefficients model
• Signal-to-noise ratio