## 1 Introduction  
Wire + arc additive manufacturing (WAAM) is a metal three-dimensional (3D) printing technique (see Figure 1). Unlike conventional 3D printing based on polylactic acid filament, WAAM uses a metal wire as the feedstock and electric arc as the heat input. Because WAAM is processed using three-axis computer numerical control routers or industrial six-axis robots, geometric parts with a high degree of freedom can be fabricated [1]. In addition, WAAM operated by robot welding techniques can overcome the size restriction constrained by bed size on other 3D printing technologies (e.g., powder bed fusion process), thereby enabling the production of large components. Furthermore, it consumes less raw material and energy compared with other metal 3D printing processes that use powders, and laser or electron beams. According to recent research studies [2, 3], the material flexibility in WAAM is expanding from typical low-carbon steel to high-performance alloys (e.g., titanium, nickel, and tungsten). Two different types of materials can be processed in one product via WAAM [4, 5]. Owing to these benefits, WAAM has attracted significant interest from researchers and manufacturers.  
Despite these benefits, WAAM presents several technical challenges and research issues that hinder its wide implementation in real industrial applications [6]. First, as recently developing technology, the optimized processing guidance depending on various materials and shapes is not well defined. Second, due to the characteristics of arc welding in WAAM such as the non-equilibrium thermal cycles, WAAM can cause defects, e.g., voids, cracks, microstructural inhomogeneity, and poor surface roughness so that post-processing (e.g., machining) must be performed. The defect deteriorates mechanical strength, incurring additional cost. Third, as the most critical problem, a single failure during WAAM can affect the entire process, which can lead to discarding the under-processed product.  
To avoid production failure during WAAM, it is very important to monitor and control the welding process. The study carried out by [PERSON] [7] mentioned that the process monitoring and feed-back control for additive manufacturing process is key success factor. Other research, by [PERSON] et al. [8], tried to use the image acquisition technique for monitoring and controlling. However, the direct monitoring of weld-pool/bead is still missing and the used AI model for image classification still has a simple structure. To overcome the limitation of monitoring and controlling in WAAM, this study provides a methodology to monitor welding processing focusing on bead generation. The proposed methodology adopts a recently developed image recognition algorithm based on artificial intelligence (AI). Among various kinds of AI algorithm, convolutional neural network (CNN) showing high performance image classification is applied for detecting problems on metal transfer and weld-pool/bead shape. Due to the complexity of the arc recognition AI model, it will be introduced in the consecutive paper.  
Through the proposed methodology, this paper proves that an AI algorithm such as CNN can be applied to the image-captured harsh environment with a high temperature. Some helpful guidance for using CNN in monitoring of arc welding is provided in this paper. The application of transfer learning of CNN shows another possibility to improve fault detection accuracy. Moreover, the developed method can be extended to real time control of welding during WAAM.