## 3 Experimental Results  
The peel strength result is measured to calculate the robustness metric, signal-to-noise ratio SNR, \(\eta\), and sensitivity, \(\beta\). In a measurement, \(V_{e}\) represents the correction of error variance that means the variation of the data measured in a sample. \(V_{e}\) reflects the variation that affects the accuracy and precision of a measurement in a controlled condition. In a robust measurement system, \(V_{e}\) is calculated with consideration of \(V_{N}\), the variation of compounded noise factor. This represents the metric for robustness called signal-to-noise ratio, \(\eta\) in decibel unit, dB. SNR example of calculation is shown below by taking the result in Table 2 for run 1:  
\[\text{SNR},\,\eta=10\,\log\,((1/r\text{-}r_{o})\,(S_{\beta}-V_{\text{e}})/V_{ \text{N}}) \tag{2}\]  
\[S_{\beta} =542.82=((4.45+4.53)5+(8.51+8.97)10+(13.51+12.94)5)^{2}/2(5^{2}+10^{2}+15 ^{2})\] \[V_{e} =S_{e}/\!f_{e}=(S_{T}-S_{\beta}-S_{\text{N}\times\beta})/4=0.0689,f _{e}=4 \tag{3}\]  
\[S_{\text{T}} =4.45^{2}+4.53^{2}+8.51^{2}+8.97^{2}+13.51^{2}+12.94^{2}=543.12\]  
\[S_{\text{N}\times\beta} =0.0180=((4.45)5+(8.51)10+(13.51)15)^{2}+((4.53)5+(8.97)10+(12.94 )15)^{2})/(5^{2}+10^{2}+15^{2})-S_{\beta}\]  
\[V_{N}=S_{e}/\!f_{e}^{\prime}=(S_{T}-S_{\beta})/5=0.05879,f_{e}^{\prime}=5\]  
Signal-to-noise ratio; SNR,  
\[\eta=10\,\log_{10}(1/2(5^{2}+10^{2}+15^{2}))[(S_{\beta}-V_{e})/V_{N}\,]=11.20 \text{ dB}\]  
Sensitivity,  
\[\beta=10\,\log(1/r\text{-}r_{o})\,(S_{\beta}-V_{\text{e}})=10\,\log_{10}(1/2(5 ^{2}+10^{2}+15^{2}))(S_{\beta}-V_{e})=-1.10\text{ dB} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & **5 mm** & & **10 mm** & & **15 mm** & & **SNR** & **Sensitivity** \\ \hline
**Run \#** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **N1** & **N2** & **N1** & **N2** & **N1** & **N2** & & **N2** & \\ \hline
1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 4.45 & 4.53 & 8.51 & 8.97 & 13.51 & 12.94 & 11.20 & \(-\)1.10 \\
2 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 6.31 & 6.12 & 12.94 & 11.95 & 18.83 & 18.15 & 10.06 & 1.85 \\
3 & 1 & 1 & 3 & 3 & 3 & 3 & 3 & 8.79 & 8.46 & 16.96 & 16.43 & 24.57 & 23.88 & 10.27 & 4.29 \\
4 & 1 & 2 & 1 & 1 & 2 & 2 & 3 & 8.39 & 8.08 & 16.50 & 15.72 & 24.08 & 23.36 & 11.55 & 4.05 \\
5 & 1 & 2 & 2 & 2 & 3 & 3 & 1 & 3.94 & 3.57 & 7.98 & 7.50 & 11.53 & 10.82 & 7.12 & \(-\)2.46 \\
6 & 1 & 2 & 3 & 3 & 1 & 1 & 2 & 7.20 & 7.05 & 13.81 & 13.52 & 19.73 & 18.91 & 6.76 & 2.41 \\
7 & 1 & 3 & 1 & 2 & 1 & 3 & 2 & 6.38 & 6.38 & 13.28 & 12.74 & 18.97 & 17.83 & 6.91 & 1.95 \\
8 & 1 & 3 & 2 & 3 & 2 & 1 & 3 & 7.97 & 7.42 & 16.27 & 15.67 & 24.76 & 23.60 & 10.11 & 4.10 \\
9 & 1 & 3 & 3 & 1 & 3 & 2 & 1 & 3.93 & 3.75 & 7.51 & 7.28 & 11.33 & 11.10 & 14.44 & \(-\)2.53 \\
10 & 2 & 1 & 1 & 3 & 3 & 2 & 2 & 6.02 & 5.19 & 12.03 & 11.19 & 16.91 & 16.60 Table 2 shows the raw data of peel strength measurement with calculated SNR and sensitivity \(\beta\) using dynamic Taguchi method. In L18 as shown in Table 2 orthogonal array, there are 108 observations implied (18 runs \(\times\) 3 signal level \(\times\) 2 noise level): The raw data are positioned under outer array below the signal and noise factor column. Optimum condition is determined from the highest peak of SNR and maximum SNR explained the power of signal is larger than power of noise, which means the variability is small. Thus, the optimum condition is influenced by noise factors. Figure 9 shows the L18 (a) SNR plot and (b) Sensitivity plot. The final step is to predict and verify the improvement in peel strength variation using the optimum level in SNR response plot. Optimum condition is taken from the highest peak of SNR plot that is A1 B1 C3 D1 E2 F2 G3. The worst condition is taken from the lowest SNR peak from each factor that is A2 B2 C1 D3 E1 F3 G2. The effect of the optimum condition is shown by the dB gain size between optimum and worst SNR. A confirmation run is done to check the SNR reproducibility of the estimation and confirmation experiment.