Page 604 - Design for Six Sigma a Roadmap for Product Development

Design Optimization: Advanced Taguchi Robust Parameter Design 559

TABLE 15.4 Inner Array

Control factors
Experiment no. A B C D E F
1 1 1 1 1 1 1
2 1 2 2 2 2 2
3 1 3 3 3 3 3
4 2 1 1 2 2 3
5 2 2 2 3 3 1
6 2 3 3 1 1 2
7 3 1 2 1 3 3
8 3 2 3 2 1 1
9 3 3 1 3 2 2
10 4 1 3 3 2 1
11 4 2 1 1 3 2
12 4 3 2 2 1 3
13 5 1 2 3 1 2
14 5 2 3 1 2 3
15 5 3 1 2 3 1
16 6 1 3 2 3 2
17 6 2 1 3 1 3
18 6 3 2 1 2 1

We again follow the two-step optimization procedure. First, we conduct
ANOVA analysis on S/N by using MINITAB. We get the ANOVA table as
follows:

Analysis of Variance for S/N, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
A 5 89.06 89.06 17.81 0.74 0.662
B 2 3.80 3.80 1.90 0.08 0.927
C 2 95.83 95.83 47.92 1.98 0.336
D 2 9.04 9.04 4.52 0.19 0.843
E 2 32.12 32.12 16.06 0.66 0.601
F 2 54.56 54.56 27.28 1.13 0.470
Error 2 48.41 48.41 24.21
Total 17 332.82

The chart in Fig. 15.16 gives percentage contribution of each factor toward S/N.
Clearly, factors C,A,F,E are important factors contributing to S/N. The fol-
lowing MINITAB data and the main-effects chart in Fig. 15.17 show that
A 5 C 2 E 1 F 1 gives the highest signal-to-noise ratio:

Least Squares Means for S/N
A Mean SE Mean
1 14.01 2.841
2 17.38 2.841
3 12.44 2.841
4 16.49 2.841
5 18.60 2.841
6 13.48 2.841