Robust design, modeling and optimization of measurement systems
نویسندگان
چکیده
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 signalresponse 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 (SN) ratio are discussed. Two different data analysis strategies – response function modeling (RFM) and performance measure modeling (PMM) – 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.
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