## 4 Conclusions  
In this study, a CNN model was developed to predict the penetration conditions in the butt welding processes at the root pass of V-groove joints. The following conclusions were drawn:  
1. In root-pass welding, penetration conditions such as PJP, CJP, and burn-through were observed. The high-speed camera footage confirmed that as the penetration condition  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline
0 & & 100 & 100 & 100 \\
1 & 97.8 & 96.2 & 100 & 98.1 \\
2 & & 100 & 86.3 & 92.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Learning performance of back-bead prediction CNN model.  
Figure 10: Confusion matrix of test dataset.  
changed, the wire extension length also varied. Consequently, changes in the welding voltage were observed.
2. The current and voltage were measured for 30 s at 10 kHz. A window size of 0.4 s was set, considering the deepest position of the weld pool. The STFT of dynamic resistance for this 0.4-s window was utilized as an input variable for the CNN model.
3. The bottom side of the test piece was scanned using a laser vision system after the welding was completed. Based on the back-bead appearance profile, PJP was classified as class 0, CJP as class 1, and burn-through as class 2; these were used as output variables for the CNN model.
4. The size of the dynamic resistance STFT was adjusted to enhance the computational speed of the CNN model. A CNN model consisting of three convolutional layers and two pooling layers was adopted, and classification training was performed on the input variables.
5. The CNN model achieved a prediction accuracy of 97.7% in predicting the occurrence of penetration conditions in weld joints. For precision, it was 100% for class 0 and class 2, and 96.2% for class 1. For recall, it was 100% for class 0 and class 1 and 86.3% for class 2. For the F\({}_{1}\)-score, it was 100% for class 0, 98.1% for class 1 and 92.7% for class 2. The developed CNN model was evaluated as a suitable model for the prediction of the occurrence of penetration conditions in weld joints and welding conditions.  
The validation process confirmed that the dynamic resistance STFTs extracted from the welding current and voltage waveforms in this study could be used to determine the penetration conditions. Moreover, it is anticipated that the real-time prediction of the penetration condition can be achieved using the transformed dynamic resistance STFT.  
In the future, real-time prediction will be necessary for determining penetration conditions. We plan to identify the key feature points of dynamic resistance related to penetration conditions. Additionally, we aim to develop an ANN model with a faster computation speed than the CNN model.  
Methodology, investigation, welding phenomena analysis, visualization, and original draft, D.-Y.K.; Welding experiment, investigation, data curation, and model development, H.W.L.; Conceptualization, supervision, and review and editing, J.Y.; Project administration, resources, data analysis, validation, and funding acquisition, J.-K.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the 2024 Ministry of Trade, Industry & Energy, and the Korea Planning & Evaluation Institute of Industrial Technology (KEIT) grant funding (Development and substantiation of joining equipment & smart joining lines for manufacturing electric vehicle chassis and battery cases with high-strength materials, 20022489, 20022438, Ministry of Trade, Industry & Energy).  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company Advance Research, HWASHIN Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.