Finding Optimum Design Parameters using Taguchi method?

Hi all,

I have a design with 6 factors (parameters) with levels varying from 8 to 4.
The goal is to find the combination of factors that gives the largest output value in the least number of experimental trials.

Most of the factors affect the output non-linearly, and many factors interact with one another non-linearly.

I have some prior distant experience with Taguchi methods, but not other DOE methods.
From what I recall, Taguchi methods work best to predict the optimal output value when the factors are linear and independent of one another (little to no interaction).

I was wondering whether people have had success implementing Taguchi methods for the design described above (i.e. multiple, multi-level, non-linear, interacting factors)? I would like to stick to Taguchi methods if possible if it will do the job.

To emphasize, although it would be nice to analyze the interaction between the factors, it is not of prime importance.
Also, accurately predicting the optimal output value is not of prime importance.
However, accurately predicting the factor levels that result in the optimal output value (or within 5% of the optimal for instance) in the least number of trials is of prime importance.



TS Contributor
I responded to this question on the Elsmar quality forum. I doubt you will see responses in this forum as Taguchi methods are not used much outside of industrial statistics.