I want to use Taguchi method for my work. I have 6 factors with different no. of levels. Factor 1 is having 6 levels and factor 3 is having 4 levels. Remaining factors have 5 levels. How should I select design of orthogonal array so that interaction among the parameters is considered for analysis?
Dear Samadhan,
A widely accepted guideline in the design and analysis of experiments is "Keep the design and analysis as simple as possible". Why do you not use a sequential strategy of experimentation (the RSM)?! RSM, properly understood, is a series of experiments, each one building upon the previous ones:
. The first phase is a factorial screening experiment that has two purposes. The first is to determine which of the factors postulated as critical really has a significant impact on the response or responses of interest. The second purpose is to propose a simple model to build a path of steepest ascent. .
. The second phase is this path of steepest ascent, where the experimenter performs a series of runs in the direction the model suggests will produce better results.
. The third phase is another factorial experiment built around the run in the path of steepest ascent with the best results. Generally, this experiment involves replication, usually via center runs, to allow a test for lack-of-fit. Once the experimenter believes that he or she is in the neighborhood of optimum conditions, one runs a design specifically intended to fit a
second-order model. The second order model allows the analyst to fit a second order model, which allows a useful approximation of the behavior of the response in the neighborhood of a mountain top (the optimal response).
. The final phase in RSM is a confirmation experiment based on the set of conditions recommended by the second-order design.
For further details see, as example, Geoff Vining (2011) Technical Advice: Design of Experiments, Response Surface Methodology, and Sequential Experimentation, Quality Engineering, 23:2, 217-220, DOI: 10.1080/15226514.2011.555280
Hi there,
if you do persist on having an experimental design with your own criteria (I mean different number of levels for factors) using a D-optimal experimental design by ((design expert software)) may be a promising suggestion. It not only helps you reduce the number of your experiments, but also benefits from the RSM design features - that is, introducing an mathematical equation for the approximation of experimental data and process optimization.
Otherwise, I suggest you use typical RSM designs such as a 5-level central composite design as Nuno Ricardo Costa suggested
Best regards.
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