## 5 Conclusions  
Considerable effort has been made so that the approach may benefit in the aviation industry via increased jet engine fans non-destructive testing accuracy and efficiency using AI technology. This paper aimed toward a new method for the AE-RTISNet inspection of the effect of aeronautics engine radiographic testing inspection system to detect defect damage from X-ray image files. This is partly a resulting use of the AE-RTISNet model based on powerful AI technology to prevent non-destructive testing human mistakes in the future. Aiming to clarify the primary issues on AE-RTISNet model testing, this paper provides the relative experimental results of four famous inspection methods, and demonstrates that the accuracy rate of AE-RTISNet was slightly higher than all methods, and had a slightly higher score than the YOLO model. Moreover, the primary types of AE-RTISNet testing and training validation approaches are analyzed and trialled in the paper. Furthermore, case studies on eight types of material defect classifier applications are performed to indicate the feasibility and effectiveness of the proposed quality validation approach. The results achieved a 0.9 mean average precision (mAP) on eight types of material defect classifier problems and required approximately 100 microseconds to develop a deep learning-based inspection procedure to augment non-destructive inspection work in an engine repair plant. Fortunately, the contribution can be achieved by replacing the inherent method with an AE-RTISNet model based on AI to achieve a certain benefit on NDT check time efficiency and save the consumption of labor resources. The experimental results used deep learning technology to determine the parameters of a customized deep learning neural network model to improve the quality assurance in NDT defect detection. The resulting AE-RTISNet successfully deployed C++ code in the TX2 development board. The model was tested and verified as being able to assist the inspector in performing the diagnosis task of the X-ray images in an aeronautics engine repair plant. In particular, the results indicated that the AE-RTISNet-based inspection system can augment the overall inspection system in relaxing the human workload and assuring quality.  
[PERSON]-[PERSON], performed the AE-RTISNet design, have written C++ code and installed in the Jetson(tm) TX2 embedded computer and wrote the paper. [PERSON] supervised the project. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Accuracy** \\ **(mAP)** \\ \end{tabular} & \
\begin{tabular}{c} **Loss** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **T(S)** \\ \end{tabular} \\ \hline Fast-RCNN & 0.78229 & 0.00238 & 0.2708 \\ Original Mask-RCNN & 0.79129 & 0.00238 & 0.3982 \\ YOLO-V2 & 0.79229 & 0.0238 & 0.0298 \\ YOLO-V3 & 0.72239 & 0.0238 & 0.0326 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inspection results of four methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Manual Inspection** & **AI-Based Inspection** \\ \hline Per 10 Min & 10 & 20 \\ Miss & 1 & 0 \\ Save \$ Million Dollars & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Manual vs. AI-based inspections.