To follow along with this example, open the Byrne Taguchi Data.jmp table found in the Design Experiment folder of the Sample Data installed with JMP. Or, generate the original design table on your own using DOE > Taguchi Arrays.
Signal and Noise Factors shows the signal and noise factors in the Byrne Taguchi Data for this example.
1.
Select DOE > Taguchi Arrays.
3.
When the Open File dialog appears, open the factors table, Byrne Taguchi Factors.jmp found in the Design Experiment sample data folder installed with JMP.
Response, and Signal and Noise Factors for the Byrne-Taguchi Example
4.
Highlight L9-Taguchi to give the L9 orthogonal array for the inner design.
5.
Highlight L8 to give an eight-run outer array design.
6.
Click Continue.
7.
Click Make Table to create the design table shown in Taguchi Design Before Data Entry.
Taguchi Design Before Data Entry
Now, suppose the pull-off adhesive force measures are collected and entered into the columns containing missing data, as shown in Complete Taguchi Design Table (Byrne Taguchi Data.jmp). The missing data column names are appended with the letter Y before the levels (+ or –) of the noise factors for that run. For example, Y--- is the column of measurements taken with the three noise factors set at their low levels.
8.
To see the completed experiment, open the data table, Byrne Taguchi Data.jmp found in the Design Experiment sample data folder installed with JMP. Complete Taguchi Design Table (Byrne Taguchi Data.jmp) shows the completed design.
Complete Taguchi Design Table (Byrne Taguchi Data.jmp)
The column named SN Ratio Y is the performance statistic computed with the formula shown below. In this case, it is the “larger-the-better” (LTB) formula, which is –10 times the common logarithm of the average squared reciprocal:
The data in Byrne Taguchi Data.jmp are now ready to analyze. The goal of the analysis is to find factor settings that maximize both the mean and the signal-to-noise ratio.
1.
Click the red triangle icon next to Model on the upper left of the data table and select Run Script. The Model script produces the Fit Model dialog shown in Fit Model Dialog for Taguchi Data.
Fit Model Dialog for Taguchi Data
2.
Click Run on the Fit Model dialog.
The profile traces (The Prediction Profiler) indicate that different settings of the first three factors would increase SN Ratio Y.
The Prediction Profiler
4.
To find optimal settings, click the red triangle on the Prediction Profiler title bar and select Desirability Functions. This adds the row of traces and a column of function settings to the profiler, as shown in Best Factor Settings for Byrne Taguchi Data. The default desirability functions are set to larger-is-better, which is what you want in this experiment. See the Multivariate Methods book for more details about the prediction profiler.
5.
Again click the red triangle on the Prediction Profiler title bar and select Maximize Desirability to automatically set the prediction traces that give the best results according to the desirability functions.
In this example, the settings for Interfer and Wall changed from 1 to 2. (See The Prediction Profiler and Best Factor Settings for Byrne Taguchi Data). The Depth setting changed from 1 to 3. The settings for Adhesive did not change. These new settings increased the signal-to-noise ratio from 24.0253 to 26.9075.
Best Factor Settings for Byrne Taguchi Data