## 5 Conclusions  
In this study, the authors developed a methodology to detect abnormalities in WAAM based on HDR camera images, and AI models based on the CNN are adopted. The tested CNN models are proven to be applicable for defect detection in WAAM. The image data gathered by the HDR camera are segmented into three areas; the RoI for metal transfer, arc shape, and weld-pool/bead. The targeted areas are the metal transfer and weld-pool/bead RoIs in this study. To improve the performance of the AI model, preprocessing is applied to the image. In designing the CNN architecture, the authors define a simple architecture of the CNN, and a predefined architecture from well-known CNN models such as VGG16 is also adopted. As VGG16 is not originally designed for defect detection, transfer learning is applied to VGG16 and then tested for its applicability. Through model training and testing, the effectiveness of the CNN model for defect detection in WAAM is confirmed. To verify the CNN model training, Grad-CAM is performed. Grad-CAM indicates that the tested CNN model is well trained to identify the designated characteristic area in the image. Additionally, Grad-CAM enables model developers to understand the training process of CNNs such that a better model can be developed.  
According to our experiments, the basic CNN model, which has a simple architecture, performed better than VGG16 with no pretraining. However, in cases where the pretrained CNN models (VGG16-PRETR and VGG16-PRETR-FINETUNE) are applied to transfer learning, they performed better than the previous ones. This means that transfer learning using the CNN can improve detection ability using pretrained features from other objects. If sufficient data are available for training, then the training to retain all weights from the beginning will yield satisfactory performance. However, if data are insufficient, then transfer learning using a pretrained CNN model is advantageous. For the model evaluation, Grad-CAM visualizes the recognized features and enables model developers to understand the CNN model, which is regarded as a black box model. In addition, Grad-CAM can support multifaceted analysis in the selection of an AI model.  
This study suggests that the AI model can be the basis of real-time monitoring for a welding process during WAAM. Defect detection can be combined with the control of process variables such as current, voltage, and feed rate. To increase the reliability of defect detection, an ensemble model that judges combinations with other process variables can be considered in the near future. For further research, the types of abnormality in WAAM must be diversified, and a generalized model must be developed to detect them. More diverse data (e.g., shape and sound) will be considered to develop more robust AI models in the near future.  
Conceptualization, J.-H.S. and D.B.K.; methodology, C.L.; investigation, M.K.; data curation, G.S. All authors have read and agreed to the published version of the manuscript. This research was supported by the IITP (Institute for Information & Communications Technology Planning & Evaluation) and NRF (National Research Foundation) of Korea. This material is supported by the National Science Foundation in USA.