Steps in Robust Parameter Design

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	Related Tools & Articles A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design Taguchi Methods: Some Technical, Cultural, and Pedagogical Perspectives More About Robust Design / Taguchi Method
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	Discussion Forum "Taguchi theory says there are two types of variables which will define a system: 1. Parameters in which level affects process variation. 2. Parameters in which process variation is unaffected by level. The idea behind robust design is to set Type 1 parameters at the level which minimizes total process variation. Type 2 parameters are used to control and/or adjust the process. Can we assume from this that Taguchi would define a stable process as 'robust'? that is, levels are chosen which will maintain the process 'on target at minimum variance' as per Wheeler's definition. Does this imply an truly stable process would self-correct?" Stable Processes - Taguchi / Robust Design

By Madhav S. Phadke

3. Steps in Robust Parameter Design
Robust Parameter design has 4 main steps:

1. Problem Formulation:
This step consists of identifying the main function, developing the P-diagram, defining the ideal function and S/N ratio, and planning the experiments. The experiments involve changing the control, noise and signal factors systematically using orthogonal arrays.

2. Data Collection/Simulation:
The experiments may be conducted in hardware or through simulation. It is not necessary to have a full-scale model of the product for the purpose of experimentation. It is sufficient and more desirable to have an essential model of the product that adequately captures the design concept. Thus, the experiments can be done more economically.

3. Factor Effects Analysis:
The effects of the control factors are calculated in this step and the results are analyzed to select optimum setting of the control factors.

4. Prediction/Confirmation:
In order to validate the optimum conditions we predict the performance of the product design under baseline and optimum settings of the control factors. Then we perform confirmation experiments under these conditions and compare the results with the predictions. If the results of confirmation experiments agree with the predictions, then we implement the results. Otherwise, the above steps must be iterated.

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