## 2 State-of-the-Art
To fabricate a satisfactory component, significant efforts have been expended in monitoring and controlling the welding process during WAAM. The considerable process variables for monitoring and controlling in WAAM are the wire feed speed, torch movement speed, tool path, voltage, current, and layer height. Early research efforts focused on identifying the relationship between process variables and fabricated component in terms of geometry [27], surface roughness [28], and mechanical properties [29]. The underlying physics mechanism for controlling WAAM has been revealed based on theory and experiment. For instance, [PERSON] et al. [30] investigated the effects of interlayer cooling time on surface quality and component geometry. They used a thermal imaging technique to obtain the surface temperature field of the deposited components. In addition, the internal flux of the weld pool was investigated through high-energy synchrotron radiation experiments [31]. [PERSON] et al. [7] summarized monitoring and controlling studies regarding WAAM using visual, spectral, acoustic, and thermal data. It was reported that combining the data from these sensors with AI techniques provided new opportunities for monitoring and controlling the welding of WAAM. Recently, many research groups have been actively performing investigations in order to develop deep learning models for arc welding monitoring and control. As a new machine learning field, deep learning indicates significant potential in defect detection trough the continuous reduction in dimensions during feature learning to avoid the effect of feature extraction on the identification results, thereby effectively improving the accuracy of defect detection. Deep learning models with digital image processing techniques are frequently adopted in various fields, and their usefulness has been proven by their outstanding performances in recognizing the environment and controlling a self-driving car [32]. Hence, they can be utilized for the anomaly detection of welding in WAAM. [PERSON] and [PERSON] used a deep neural network to predict bead geometry with respect to different process variables [33; 34]. However, the prediction of geometry was restricted to dimensional values such as the width, height, and toe angle of the bead. These values cannot represent the possible variety of bead defects. To extend the capability of anomaly detection, researchers have started to use image data [35; 36] from various types of vision sensors. [PERSON] et al. [35] proposed a new weld-pool data collection method that can achieve high model classification performance. They used a convolutional neural network (CNN) to classify weld seam quality. [PERSON] et al. [36] predicted the trend of weld-pool features using Pred-NET, a CNN algorithm. [PERSON] et al. [37] achieved high prediction accuracy on a relatively small dataset of weld defects based on the VGG-16 full CNN. Nevertheless, the sample size was relatively small in some areas, which affected the prediction results. Therefore, many researchers use transfer learning to overcome the problem of small samples and use deep CNN models trained on ImageNet as feature extractors to migrate to small datasets in other disciplines with favorable results [38]. It is noteworthy that these small datasets are completely different from ImageNet. [PERSON] et al. [39] obtained an identification accuracy of 97.041% after investigating medical imaging using a transfer learning method. [PERSON] et al. [40] investigated automatic surface detection of deep convolutional activation feature models based on deep transfer learning. Compared with other methods, the accuracy of [PERSON]'s method improved by 0.66-25.5% in the detection of defects during classification. [PERSON] et al. [41] used a transfer learning algorithm that modified the structure of the existing MobileNet to monitor welding defects in a small dataset. The image-based deep learning method contributed positively to feature learning and does not affect the prediction results.