# Review on Quality Control Methods in Metal Additive Manufacturing
## 4 Experimental Quality Monitoring Methods in Metal AM and Welding
### Experimental Quality Monitoring Methods in Metal AM  
Image monitoring is a common experimental quality monitoring method for anomaly detection in metal AM. Optical [59; 60; 61], X-ray [62; 63] and thermal [64; 65; 66; 67; 68] image sensors have been utilized to capture the images with anomalies. It was experimentally observed that anomalies, such as the melt pool [65; 69; 70; 71], plume and spatter [59; 60; 71; 72], and keyhole [62; 63], can cause porosity, residual stress, and dimensional errors in both PBF and DED products. [PERSON] et al. [67] comprehensively monitored the shape, size, and number of spatters during the metal PBF process using a high-speed infrared camera. It was observed that the number and the size of the spatters are affected by the laser power and build rate. During deposition, the spatter particles scattered to the surface of each layer and agglomerated with each other on the surface, causing layer delamination. Especially, large spatter particles caused crack and separation at the edge of the product. [PERSON] et al. [59] applied a high-speed near-infrared (NIR) camera to capture optical images of plume and spatter. The authors applied improved deep belief network (DBN) to recognize the captured images. Because the proposed DBN does not need a feature extraction stage, it has potential to implement in situ plume and spatter monitoring for PBF processes. It was observed that the proposed DBN showed 83.40% accuracy in recognizing plume and spatter images in real time. [PERSON] et al. [60] quantified the plume and spatter sizes through optical images and found that the tensile properties of the product decreased when the plume and spatter were mixed into the product during the process. [PERSON] et al. [61] integrated an additional illumination source for a high-speed complementary metal-oxide semiconductor (CMOS) camera to capture melt pool images at high sampling rates. [PERSON] et al. [66] connected a high-speed infrared camera to a field-programmable gate array (FPGA) to implement a high-speed data acquisition system. Additionally, a feedback closed-loop system was designed to control process parameters such as laser power, scan speed, and single-cladding track height [73]. [PERSON] et al. [62] applied high-speed X-ray image sensors to capture the keyhole phenomenon during a DED process and experimentally found that the laser power was dominant around the keyhole depth. Across the monitoring studies, it was found that melt pool, plume, spatter, and keyhole occur together during deposition. Other studies showed that applying image sensors is also feasible in identifying dimensional errors during metal AM [74; 75; 76]. By monitoring and characterizing the optical images of a single deposited layer in real time, [PERSON] et al. [74] identified the dimensional errors of each layer during a PBF process and predicted the dimensional error of the final products. Some experimental studies have reported that the plasma spectrum and acoustic signals can also be used for monitoring metal AM processes. The width of the cladding track [77] and elemental composition [3; 78] of the product can be identified from the plasma spectrum. [PERSON] et al. [77] focused on using optical emission spectroscopy (OES) for single-cladding track monitoring during the DED process. The authors focused on the spectral intensity and electron density of the plasma spectrum, which can indicate the width of a single-cladding track. A spectrometer with a wavelength range of 200-950 nm was applied to the DED machine, off-axial to the arc torch, and the iterative discrete wavelet transform was selected to denoise the spectrum data of the arc [79]. It was observed that the plasma spectrum can be used for in situ geometry monitoring. [PERSON] et al. [78] applied laser-induced OES to predict the binary alloy composition during the DED process. Support vector regression (SVR) was used to recognize the pattern between the plasma spectrum and elemental composition in real time.