# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding
### Experimental Quality Monitoring Methods in Welding  
Several experimental studies have been conducted on welding to identify anomalies using image data. Similar to metal AM, the keyhole [87, 88], melt pool [89, 90], and distortion [87, 91] were identified as welding anomalies by image monitoring. [PERSON] et al. [91] proposed a thermal image monitoring system to identify the distortion of the cladding track during the laser cladding process. The results show that the distortion of the cladding track is directly related to the clad heat. [PERSON] et al. [57] observed that the materials were excessively deposited to the corners of the product due to the nonuniform powder feed rate during the laser cladding process. As the geometry of the product became more complex, the excessive material deposition increased, which led to a geometrical error. Based on the monitoring result, the authors developed a real-time feed rate control algorithm to optimize the process. The manufacturing test result showed that the excessive material deposition was eliminated by applying the algorithm. Examples can also be found in monitoring common anomalies.  
The plasma spectrum was also utilized to monitor anomalies during the welding process. [PERSON] et al. [92] characterized keyholes using plasma emission monitoring. Three optical sensors covering different wavelength ranges were applied to increase the possibility of collecting keyhole features. The authors proposed another monitoring system in a subsequent study [93], which reduced the keyhole identification time for real-time spectral emission monitoring. [PERSON] et al. [89] demonstrated that analyzing the plasma spectrum from the melt pool can be a potential method for monitoring the melt pool properties in laser welding.  
There were experimental studies focused on utilizing acoustic signals to monitor the welding. [PERSON] et al. [94] characterized keyhole shapes using acoustic signals. [PERSON] et al. [95] noted that acoustic signals from a keyhole contain dynamic change information of the keyhole to predict the keyhole geometry. [PERSON] et al. [96] predicted the welding temperature using an acoustic signal. [PERSON] et al. [54] applied acoustic signals for keyhole and spatter monitoring. [PERSON] et al. [97] evaluated the changes in the weld size and localized discontinuities of the welded area through acoustic signals. [PERSON] et al. [98] found that the use of acoustic signals for welding could identify deformation and cracks during welding.  
Several experimental studies applying multisensors for laser welding have been reported. [PERSON] et al. [88] applied multisensors in order to identify the interactions between the laser beam and the preheated filler wire in a laser hot-wire welding process. By applying a high-speed optical camera, the authors found that preheating the wire reduced the heat input during welding and laser beam power consumption and produced a deeper penetration. Additionally, by applying a spectrometer, the authors observed that the electron temperature of the plasma could be used for real-time monitoring of the variation of the welded area features and the formation of weld defects. [PERSON] et al. [90] applied a multisensor monitoring system to investigate the influence of the process parameters on the melt pool behavior during laser powder welding. An infrared camera was used to study the temperature distribution, size, and cooling rate of the melt pool. Additionally, a pyrometer was used to measure the variation in the brightness temperature of the melt pool, observing that the laser power and carrier-gas flow rate were dominant in the melt pool behavior. Some studies focusing on the use of acoustic signals have also applied additional image sensors to ensure the accuracy and detectability of the monitoring system [54, 94, 95].