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Chapter 6- Analysis of Variance (ANOVA) (eChapter from A Primer on the Taguchi Method, 2nd Edition) Image

Chapter 6- Analysis of Variance (ANOVA) (eChapter from A Primer on the Taguchi Method, 2nd Edition)


Author(s)/Editor(s): Ranjit K Roy
Published By:
SME

Published: 03/01/2010
Product ID:
BK09PUB26_E_CH-6
ISBN:
978-0872638648

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Description

Taguchi replaces the full factorial experiment with a lean, less expensive, faster, partial factorial experiment. Taguchi’s design for the partial factorial is based on specially developed orthogonal arrays (OAs). Because the partial experiment is only a selected set of the full factorial combinations, the analysis of the partial experiment must include an analysis of confidence to qualify the results. Fortunately, there is a standard statistical technique called analysis of variance (ANOVA) that is routinely used to provide a measure of confidence. The technique does not directly analyze the data but rather determines the variability (variance) of the data. Confidence is measured from the variance. Analysis provides the variance of controllable and noise factors. By understanding the source and magnitude of variance, robust operating conditions can be predicted. These uncontrollable factors are called the noise factors, and their effect on the outcome of the quality characteristic under test is termed “noise.” The signal-to-noise ratio (S/N ratio) measures the sensitivity of the quality characteristic being investigated in a controlled manner to those influencing factors (noise factors) not under control. The concept of S/N originated in the electrical engineering field. Taguchi effectively applied this concept to establish the optimum condition from the experiments. The aim of any experiment is always to determine the highest possible S/N ratio for the result. A high value of S/N implies that the signal is much higher than the random effects of the noise factors. Product design or process operation consistent with highest S/N always yields the optimum quality with minimum variance.