## 2 State-of-the-Art  
To avoid any defect in WAAM product, nondestructive evaluation methods (e.g., eddy current, ultrasonic, and industrial computerized tomography) have been utilized. However, they increase the total manufacturing cost and decrease the effectiveness of WAAM [9]. Furthermore, these methods can be performed after a product is additively manufactured, which means that the product cannot be fixed during WAAM in real time. In this context, real-time monitoring and control approaches are being considered as promising solutions to maintain process stability and part quality [10; 11; 12]. Regarding part quality, many research works emphasize the sensing and tracking of weld seam [13]. The machine vision technologies are adopted in arc seam tracking [14]. Figure 1: (**a**) Multihead WAAM system and (**b**) wire + arc additively manufactured structures. Due to complication of classification of arc in WAAM, this topic will be handled in another paper by the authors. [PERSON] et al. [15] and [PERSON] et al. [16] explain the application of deep learning for weld pool. The metal transfer and bead shape as well as weld pool should be monitored concurrently, a process which is still lacking. In the defect classification stage, which determines part quality, a reliable classifier must be designed to distinguish between different types of defects. Many researchers have investigated and discussed the development of different classification algorithms. Machine learning methods such as artificial neural networks (ANNs), support vector machines (SVMs), and fuzzy systems are the most widely used in welding defect recognition. The main application of fuzzy theory in welding defect detection has been reported in the late 1990s [17], where [PERSON] [18] investigated a fuzzy expert system method for classifying defect types with better classification accuracy than the fuzzy k-nearest neighbor and multilayer perceptron. Furthermore, [PERSON] [19] investigated a new type of complex classifier comprising fuzzy systems and ANNs. However, a tradeoff exists between the accuracy and interpretability of fuzzy defect detection. The SVM and ANN are the most typically used methods for defect detection. [PERSON] et al. [20] simulated welding parameters (welding time, current, voltage, thickness, etc.) to establish a method for evaluating the welding quality of an ANN. [PERSON] et al. [21] modified an ANN to improve the detection accuracy of individual and overall defect characteristics. [PERSON] et al. [22] investigated adaptive tissue and adaptive feedforward neural networks to identify the essential features of defects and effectively reduce identification errors. To achieve high accuracy and improve classification efficiency, [PERSON] et al. [23] proposed an automatic classification algorithm that combines principal component analysis and the SVM to select the optimal dataset. Inspired by this, [PERSON] et al. [24] applied a bee algorithm to extract defect features and used a hierarchical multiclass SVM to achieve a maximum accuracy of 95%. [PERSON] et al. [25] built an automatic defect identification system for solder joints by extracting the texture features of weld defects. [PERSON] et al. [26] combined ELM and M-estimation and proposed a new ME-ELM algorithm. This algorithm can effectively improve the anti-interference and robustness of the model, and it provides high accuracy in predicting welding defects. Typically, these shallow machine learning methods are combined with the feature extraction process to ultimately affect the machine-learning prediction results. However, it is difficult to determine the features to extract in WAAM, not regular welding. Therefore, to implement automatic feature learning and weld defect prediction, an efficient deep learning method must be designed. However, these approaches have not been extensively investigated because of insufficient knowledge regarding machine vision and artificial intelligence (AI) in WAAM.