Basic Taguchi DOE Templates

Introduction - Taguchi Methods

Dr. Genichi Taguchi was a Japanese engineer and quality consultant who codified his consulting techniques into a formal methodology. This generic engineering tool, called the Taguchi method, is used for system studies. The purpose of these studies is to reduce system variability while simultaneously decreasing costs and increasing productivity.

The features of the Taguchi method include:

• extensive use of experimental design
• separation of factors by role (control and noise)
• use of measures of variability as responses
• dual objectives of process centering and noise minimization
• use of loss functions for economic justification of each application

The following steps outline the Taguchi Design of Experiments method:

1. Identify what are the control factors and what are the noise factors. Noise factors are costly and difficult to control, while an ideal control factor is easy to control precisely. Noise factors can include environmental factors, component deterioration, and process variation.
2. Identify the levels for the control and noise factors.
3. Identify the responses of interest and determine a quality criterion for each response. Does the system require that the response match a specified value, be as large as possible, or approach zero?
4. Construct a design for the control factors (inner array) and a design for the noise factors (outer array). The inner-array is selected based on the number of control factors. Typically, interactions are assumed to be negligible, but if they are to be included, then a larger design may be required. The outer array simulates the various conditions that the noise factors would produce in reality. For each run in the inner array, all runs in the outer array are carried out.
5. Conduct the design of experiments. Typically, you complete all the runs in the outer array before proceeding to the next run of the inner array. The runs for the inner array and outer array are (ideally) randomized separately.
6. Evaluate the performance statistic for each run of the inner array. These measures become the responses for the inner array. The performance statistics include Mean(Y), StDev(Y) or Ln(StDev), and Taguchi's signal-to-noise ratios. Some practitioners prefer to use Ln(StDev) as a variance stabilizing transformation. Taguchi signal-to-noise ratios include Nominal is Best, Larger is Better and Smaller is Better. Signal-to-noise ratios are always maximized.
7. From the Pareto of Deltas (Main Effects), Pareto of ANOVA SS (Sum-of-Squares) % Contribution, Main Effects & Interaction Plots and Prediction Equation model, determine the new set points for the control factors. Taguchi’s two step optimization first finds control settings that maximize the SN Ratio (and/or minimize the StDev), then if available, factors that move the mean to Target without affecting the SN Ratio.
8. Confirm that the new settings meet target, dispersion and loss criterion with follow-up experimental runs.

Taguchi Orthogonal Arrays are a cookbook of designs that are similar to Full & Fractional-Factorial and Plackett-Burman designs. For example, the L4 design is 2-level, 4 runs with up to 3 factors; L8 is 2-level, 8 runs with up to 7 factors; L9 is 3-level, 9 runs with up to 4 factors. Taguchi designs use 2-level coded values of 1, 2 instead of the orthogonal coding -1, +1 and 3-level coded values of 1, 2, 3 instead of -1, 0, +1.

Interactions are typically assumed to be negligible compared to main effects, but some designs permit the analysis of all interactions or aliased interactions. Selection of aliased interactions is more economical than all interactions, but they should be used with caution. Process knowledge, engineering or theory are used to make the selection and assume that the chosen interaction is dominant and the others are negligible. Aliased interactions are often associated with the largest main effects. Confirmation runs should always be used to validate the model.

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