## 8 Conclusions  
In this study, to enhance and speed up the Phased Array Ultrasonic Testing (PAUT) evaluation process, an algorithm to size and to determine the acceptance of flaws within a weldment is developed. A weldment wherein a flaw is embedded is evaluated by the size of the flaw. When the flaw does not meet the acceptance criteria described in an adequate international standard, the weldment is determined unacceptable. Therefore, an algorithm that determines a flaw in a weldment referring to the acceptance criteria should be developed.  
For this research, the crucial international standards when evaluating Liquefied Natural Gas (LNG) storage tanks with PAUT are listed. API 620 Annex U contains the acceptance criteria for flaws in weldments. The sizing of a flaw in an LNG storage tank PAUT evaluation must follow the sizing methods introduced in ISO 13588 and ISO 11666.  
Referring to the international standards, a total of 498 S-scan PAUT simulation images were designed and made. All the PAUT S-scan images were designed and made by PAUT simulation with EXTENDE CIVA. The flaws embedded in the simulation had the same rectangular shape, with different sizes. The image dataset was then divided into two different datasets, one for training and the other for validation. Then, the images were masked and annotated using VGG Image Annotator (VIA). The developed algorithm  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Precision** & **Recall** \\ \hline Class B: Acceptable Flaws (Acceptable) & 82.86\% & 100\% \\ Class C: Not Acceptable Flaws (NA) & 96.43\% & 87.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Precision and recall of the trained model.  
Figure 10: Confusion matrix of the trained model.  
using Mask R-CNN was trained with two different classes of S-scan, acceptable and not acceptable (NA).  
After the training, 60 S-scan images, 29 acceptable flaw images, and 31 not acceptable flaw images were used for the performance testing of the trained model. For Class B, which was acceptable flaws, the precision was 82.86% and the recall was 100%. For Class C, which was unacceptable flaws, the precision was 96.43% and the recall was 87.10%. The testing result shows that Mask R-CNN was properly trained.  
The result of this research proves the applicability of ANN in PAUT S-scan data sizing. Although the model showed good performance, it still needs some improvements before its application in the field. Most importantly, the dataset was collected from the PAUT simulation. For the application of the model, the model must be trained with actual PAUT data. With actual PAUT data, the model will generate more practical results and become part of a more efficient inspection system.  
Conceptualization, S-J.S., S-E.L. and J.P.; methodology, S-J.S. and S-E.L.; software, H.-J.K., S-E.L. and J.P.; validation, S-J.S., S-E.L., J.P., H.-J.K. and Y.-T.Y.; formal analysis, S.-E.L.; investigations, S.-E.L.; resources, S.-E.L. and J.P.; data curation, S.-E.L.; writing--original draft preparation, S-E.L.; writing--review and editing, S.-E.L. and S-J.S.; visualization, S.-E.L.; supervision, S.-J.S. and H.-J.K.; project administration, Y.-T.Y.; funding acquisition, S.-J.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1I1A1A01073812).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.