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Robust Parameter Design Reducing Variation

The aim of the parameter design is to establish a combination of levels of the design factors which leads to a minimum in the variability around a mean, minimum or maximum value in the response. The idea behind the parameter design is to find a set of analytical conditions which are both functional i.e. they fulfil the objective) and robust i.e. they are not sensitive to noise factors), as far as possible. [Pg.74]

Taguchi classified robust designs according to the main objective when measuring the response in three basic types nominal-is-besf (minimal variability around a target value), larger-the-better (minimal variability for a maximum in the response) and smaller-the-bettef (minimal variability for a minimum in the response). [Pg.74]

in the nominal-is-besf case, the signal-to-noise (s/n) ratio is defined, for each run of the design matrix, as [Pg.75]

For the larger-the-betteE and smaller-the-bettef cases, the respective expressions for the s/n ratios are given below. In all cases, y,- are the observed responses and n,- the number of replicates for each run. [Pg.75]

According to these definitions, in the smaller-the-betteE case, the s/n ratio increases as the mean and variability decrease. Similarly, in the darger-the-better case, the s/n ratio increases as the mean increases and variability decreases. [Pg.75]


See other pages where Robust Parameter Design Reducing Variation is mentioned: [Pg.74]    [Pg.163]    [Pg.74]    [Pg.163]    [Pg.534]   


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