###### Abstract
The evaluation requires much experience, since it must correctly follow the internationally implemented standards, and has a high cost, so attempts to automate this procedure with ANN or other Artificial Intelligence (AI) techniques have been previously made. [PERSON] et al. [5] used probabilistic neural networks for ultrasonic weldment flaw signals. [PERSON] et al. [6] used the system invariant method and residual neural network for ultrasonic flaw classification in weldments. [PERSON] et al. [7] used the dropout technique for ultrasonic flaw classification in weldments. [PERSON] et al. [8] also used a Convolutional Neural Network (CNN) for ultrasonic flaw classification in noisy conditions. [PERSON] et al. [9] used neural networks for feature selection in metal weldment UT. [PERSON] et al. [10] also used ANN for feature selection in an ultrasonic A-scan. [PERSON] et al. [11] used a CNN for ultrasonic A-scan flaw detection. [PERSON] et al. [12] used the EfficientDet Network for flaw detection in PAUT B-scan images. [PERSON] et al. [13] used a CNN for flaw detection in PAUT B-scan images. [PERSON] et al. [14] used a two-stage ANN for flaw classification in PAUT S-scan images. Most of the previous research, except for [5], focused on the detection of the flaw or classification of the flaw using neural networks. Detection of the flaw mainly focuses on determining whether the flaw exists in various scanning methods. Classification of the flaw seeks to distinguish the type of the flaw among various types of existing flaws. However, these attempts cannot practically evaluate whether the flaw is dangerous, because they lack the procedure of sizing of the flaws detected by PAUT. The sizing of PAUT outcomes  
Figure 1: Overall procedure of PAUT. according to API 620 [15] and ISO 11666 [16] aims to set up a threshold for similar types of flaws so that the flaws can be determined acceptable or not. When the sizing process of flaw images obtained by PAUT can determine the flaw's acceptability following the size of the flaw, then the class of the flaw can be divided into two variables, acceptable or not acceptable. Until now, the sizing of flaw data obtained by PAUT was not considered important, but the sizing procedure must be handled and automated with the ANN so that the efficiency of PAUT can be enhanced. Mask R-CNN is a two-stage object detection model that is trained with images that are masked and annotated by classes. If the classes when training Mask R-CNN are labeled according to the sizes of flaws, Mask R-CNN will be able to determine the flaws' acceptability. In this paper, the flaw will be determined according to the reasonable acceptance criteria with a two-stage object detection ANN model, Mask R-CNN. The data will be designed and obtained using the renowned ultrasonic simulation tool, EXTENDE CIVA, following the acceptance criteria that are described in this paper below, referring to API 620 Annex U. Since the ultrasonic S-scan image completely volumetrically presents both vertical and horizontal directions of the weldment and the flaw, a simulated ultrasonic S-scan will be a useful data tool for the training of Mask R-CNN. The data will then be masked and annotated with an image annotation tool called VGG Image Annotator. The class of the flaw data obtained with PAUT must be divided into two classes, acceptable or not acceptable (NA), according to the size of the flaw. After the masking process, the data will be used to train the Mask R-CNN model and the performance of the trained model will be tested.