Dispersion Effects in Robust-Design Experiments with Noise Factors

David M. Steinberg · Dizza Bursztyn

65 citations0/0 files storedJournal of Quality Technology

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Optimal Experimental Design Methods Advanced Statistical Methods and Models Probabilistic and Robust Engineering Design

Abstract

Robust-design experiments can be a very useful tool for improving quality. They enable engineers to reduce the variance of important quality characteristics by identifying design factors with dispersion effects and guiding the choice of nominal levels of those factors. Robust-design experiments are especially effective when it is possible to build some variation directly into the experiment by including noise factors – factors that are impossible or too expensive to control during actual production or use. When noise factors are included, it is important to model their effects explicitly in the subsequent analysis. We present two examples in which failure to do so leads to incorrect conclusions about dispersion effects.

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References

1990Quality Engineering Using Robust Design 1959Use of Half-Normal Plots in Interpreting Factorial Two-Level Experiments 1992Taguchi's Parameter Design: A Panel Discussion 1990Combining Taguchi and Response Surface Philosophies: A Dual Response Approach 1988Signal-to-Noise Ratios, Performance Criteria, and Transformations 1990Computer Experiments for Quality Control by Parameter Design 1992Response Surface Alternatives to the Taguchi Robust Parameter Design Approach 1991Economical Experimentation Methods for Robust Design

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2001Modeling and Analysis of Dynamic Robust Design Experiments 1996A critical look at Taguchi's modelling approach for robust design 1998Joint Estimation of Location, Dispersion, and Random Effects in Robust Design 1993NOISE FACTORS, DISPERSION EFFECTS, AND ROBUST DESIGN 1998A comparison of methods for analyzing data from a three component mixture experiment in the presence of variation created by two process variables

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