# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Processsurveillance and Control  
To maintain the desired process behavior for the building process and keep a desired geometry, surveillance of the welding process can be a crucial part. Some working groups have established a multi-sensor framework for process surveillance [125; 126; 127]. Optical observations are one core point in the research [128]. [PERSON] et al. showed that optical surveillance of the wire tip and the wire tip deviation is possible [82]. The occurring deviation of the wire tip can lead to incorrect geometry of the part and internal faults. This deviation occurs due to the winding of the filler material onto a spool or into a barrel. To achieve the best winding, pre-stress is applied, which is released during the welding process. In addition, the winding causes a deformation of the wire which supports the deviation phenomena. In some automated welding processes, wire straightening showed beneficial effects [82]. Due to the comparison to other additive manufacturing processes with high energy input, one critical point for the manufacturing of large parts is the accumulation of heat. This accumulation leads to a loss of geometrical corrections and to a change in the mechanical properties. One way to overcome this problem is to introduce waiting times in the manufacturing process. Due to, in most cases, limited thermal conductivity of the welded parts, the waiting times increase with the build height. The second option is active cooling of the part due to the manufacturing process. The influence of external cooling on the properties and the surface of WAAM parts is currently under investigation [28; 61; 129; 130]. [PERSON] et al. integrated an active air cooling in the manufacturing process and showed that this can lead to a significant decrease in thermal accumulation and a decreased substrate temperature [129]. Furthermore, they integrated this into a numerical model [130]. [PERSON] et al. showed that, in addition, the usage of a water bath or an aerosol can increase the cooling rate in comparison with active air cooling. However, they stated that in the case of a water bath, handling of the part during manufacturing is more difficult [61]. In welding technology, a basic way to achieve certain weld properties is to maintain the interlayer temperatures in multi-run welds. This method can be adopted in additive manufacturing as well and leads to the development of a WAAM system with interpass temperature control (such as in [PERSON] et al. [131]). One modern method of process surveillance is automated detection of the weld pool geometry and extending this to monitor the cooling conditions. This can be achieved with two different approaches. First, the surveillance can be carried out by a couple of cameras, including process and material-dependent filters, as was described by [PERSON] et al. in [26], [PERSON] in [132] and others in [133; 134; 135; 136]. In most cases, conventional CCD cameras or similar cameras are used and will be overexposed to light emitted by the welding process. Therefore, suitable filters need to be used. [PERSON] et al. showed the identification of possible filters for a low-alloy steel and an M21 shielding gas on a partial spectrum of the emitted light (Figure 14) [26]. Furthermore, they showed that two camera differential methods can lead to proper thermal measurements. Figure 15 shows the calculated results for their setup exemplary. [PERSON] et al. used a high dynamic range two-colored pyrometric camera for the measurement of the melt pool temperature of a titanium alloy during additive manufacturing and used a scanned spectrum of the light emitted from the process to identify a suitable filter [132]. [PERSON] et al. measured the melt pool width using a mask R-CNN-based welding image technique [136]. [PERSON] et al. showed that an optical surveillance system can be used  
Figure 14: Partial spectrum from 600 nm to 900 nm of arc plasma with the selected band passes plotted [26]. Figure 15: Calculated temperature distribution in the weld seam [26].