# PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design
## 5 Results and Discussion  
This section explains the PFDA-FEMA method results obtained, and the experiments performed to validate it. Likewise, a discussion based on the current FMEA method gaps against the PFDA-FMEA method is provided; additionally, methods in [65; 66], and the conventional FMEA method are compared, and advantages of the PFDA-FMEA method are highlighted. Table 6 shows the PFM ranking, as part of the numerical case applied to an electronic devices' PD process. The major risk of the PFM is represented by the lowest number on the PFM ranking, and the risk is less while the ranking is bigger. Based on the revised literature in previous sections, it is observed that the FMEA method has been criticized, mostly because of the uncertainty in the risks ranking; therefore, the proposed PFDA-FMEA method has advantages against the current FMEA method, covering uncertainty gaps. Although there are currently different hybrid methods proposed to improve the FMEA, some recent works use hybrid methods focused on the weights and RPN ranking like [67]; in contrast, the proposed PFDA-FMEA method blends the Pythagorean Fuzzy Sets, Dimensional Analysis, and FMEA, improving the current FMEA method. In other words, PFDA-FMEA removes the uncertainty of risk assessment. Aiming to illustrate the validation of the PFDA-FMEA method, a comparison was made using a similar method found in the literature. This discussion used the Pythagorean Fuzzy MOORA method (PF-MOORA) by [65] and the Pythagorean Fuzzy Entropy (PF-Entropy) by [66]. Table 7 shows the results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**PFM ID** & **PFDA-FMEA Index** &
\begin{tabular}{c} **PFDA-FMEA PFM** \\ **Ranking** \\ \end{tabular} \\ \hline PFM1 & \(1.3\times 10^{-6}\) & 16 \\ PFM2 & \(2.1\times 10^{-6}\) & 14 \\ PFM3 & \(2.1\times 10^{-5}\) & 11 \\ PFM4 & \(1.4\times 10^{-6}\) & 15 \\ PFM5 & \(2.2\times 10^{-7}\) & 18 \\ PFM6 & \(1.1\times 10^{-6}\) & 17 \\ PFM7 & \(7.9\times 10^{-5}\) & 6 \\ PFM8 & \(7.8\times 10^{-4}\) & 2 \\ PFM9 & \(9.9\times 10^{-5}\) & 5 \\ PFM10 & \(4.4\times 10^{-5}\) & 8 \\ PFM11 & \(1.4\times 10^{-4}\) & 4 \\ PFM12 & \(3.1\times 10^{-6}\) & 13 \\ PFM13 & \(3.2\times 10^{-5}\) & 10 \\ PFM14 & \(1.2\times 10^{-7}\) & 19 \\ PFM15 & \(4.5\times 10^{-5}\) & 7 \\ PFM16 & \(2.5\times 10^{-4}\) & 3 \\ PFM17 & \(7.5\times 10^{-6}\) & 12 \\ PFM18 & \(4.4\times 10^{-5}\) & 9 \\ PFM19 & \(3.3\times 10^{-3}\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PFDA-FMEA results and risks ranking. The similitude between the results while using fuzzy methods is solid, showing a close trend and pattern. Furthermore, the PFDA-FMEA method offers more accuracy than the rest of the compared methods, mainly because of the integration of PFS and DA, giving the advantage of solving problems where big accuracy is required. In addition, the comparison between the methods reveals that PFM19 has the same ranking for PF-Entropy and PFDA-FMEA. In this sense, the main risk identified is PFM19 (bad quality of raw material) for the PD process analyzed in this paper. In a graphical representation, Figure 2 shows the comparison between the three methods in Table 7. Based on this comparison, a similarity while using fuzzy techniques is observed, where the results are consistent in providing ranking of PFMs. In this mode, a risk assessment study can be addressed using the PFDA-FMEA method, offering high accuracy. Finally, the expectation in the PD process is to improve risk assessment of the electronic devices environment using the PFDA-FMEA proposed method.