Background
Taguchi robust design is used to find the
appropriate control factor levels in the design to make the system
less sensitive to variations in uncontrollable noise factors (i.e.
to make the system robust).
Consider an experiment that seeks to determine
a method to assemble an elastomeric connector to a nylon tube
while delivering the requisite pull-off performance suitable for
an automotive engineering application.*
The primary design objective is to maximize
the pull-off force, while secondary considerations are to
minimize assembly effort and reduce the cost of the connector
and assembly.
The controllable and noise factors are:
For the inner (control) array, Taguchi OA L9
(3^4) is used. For the outer (noise) array, a two level full
factorial design is applied.
Experiment
Design
The experimenters use DOE++ to
design a Taguchi robust design. The settings and factor properties used
for the inner (control) array are shown next.
The settings and factor properties used for the outer
(noise) array are shown next.
The design matrix and the response data are given
in the "Taguchi Robust Design Example" Folio.Analysis
Part I
Step 1: After
performing the experiment according to the design and recording
the results, the experimenters enter the data set into the Standard Folio, as shown next.
[Click
to Enlarge]
Step 2: The
larger-the-better ratio is chosen for the analysis.
Step 3: The data set is
analyzed with the default risk (significance) level of 0.1,
using individual terms, for each of the three responses (Y Mean,
Y Std and Signal Noise Ratio).
Step 4: The Main Effect
plot is created for the Y Mean (pull-off force) response using
the Data Means setting, as shown next.
Step 5: The Main Effect
plot is created for the Y Std response using the Data Means
setting, as shown next.
Step 6: The Main Effect
plot is created for the Signal Noise Ratio response using the
Data Means setting, as shown next.
Conclusions
Since the goal is to maximize both signal-to-noise ratio and the
pull-off force, some trade-off may need to be made in the
selecting of factor settings. Examining the main effect plots
for Y Mean and Signal Noise Ratio shows that the medium level
for A is clearly the best choice for maximizing signal-to-noise
ratio (robustness) and the average pull-off force (Y Mean). It
also has a relatively low standard deviation value. For the wall
thickness, B, the medium and high level are slightly better than
the low level for signal-to-noise ratio; however, the medium is
preferred to high level in order to maximize the average
pull-off force. From the main effect plot, the better settings
for factors C and D can also be determined. In the final
analysis, the best settings to maximize signal-to-noise ratio
are A (medium), B (medium), C (deep) and D (low), based on the
experimental results for maximizing pull-off force.
*
G. Taguchi, “The Taguchi Approach to Parameter Design,”
Quality Progress, Dec. 1987, pp.
19-26. | 
