## 7 Summary and Perspective  
The latest research integrating ML techniques for in situ monitoring and control of the L-PBF process has been reviewed in this work. First, a description of the L-PBF process signature and the detectable defects was discussed, highlighting the difference between the level of details in each process signature. Afterward, the most common sensors currently used for in situ monitoring were discussed and compared in terms of applications and specifications. Then a detailed categorization of the different ML techniques used for L-PBF defects detection was presented. Finally, this review article classified the ML algorithms in the literature according to sensor type and monitored process signature. Moreover, the application of ML-based control was compared, and more research and investigation in this field is required to overcome the limitations of computationally expensive physics-based models.  
It was observed that if an acoustic sensor is being used to monitor the L-PBF process, analyzing the collected AE signals in the frequency domain resulted in higher classification accuracy. It was also observed that the highest accuracy could be obtained when deep learning neural networks are applied. When DSLR cameras are being used to monitor the  
Figure 26: Developed architecture for control systems by [PERSON] et al. [21].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Machine Learning Technique** & **Process** & **Control Strategy** & **Data** & **Reference** \\ \hline Sequential decision making & & & & \\ through the Markov decision & L-PBF & Optimal control & Imaging & [PERSON] et al. [154] \\ process framework & & & & \\ \hline CNN & FDM & Adjustment of filaments & & \\  & & flow rate & Imaging & [PERSON] et al. [155] \\ \hline Radial basis function networks & & & & \\ (RBF) & L-PBF & Feedforward control & Temperature using & \\  & & & & \\ \hline Support vector machine (SVM) & FDM & PID control & Imaging & [PERSON] et al. [157] \\ \hline Reinforcement learning (RL) & L-PBF & &
\begin{tabular}{c} Corrective actions to \\ prevent the onset of \\ defects \\ \end{tabular} & Acoustic emissions & [PERSON] et al. [158] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overview of ML-based process control of AM processes.  
scanned layer and powder bed layers, it was observed that the supervised approach using DCNN resulted in relatively higher classification accuracy of defects. CNN was the most suitable ML algorithm used to monitor meltpool signatures of the L-PBF. However, it was noted that mounting the camera coaxially resulted in higher defects classification rates. Linear regression models resulted in high classification accuracy when used to classify defects detected by pyrometers and photodiodes.  
A major challenge with the ML algorithm examples described in this review article is that they are only applicable to the specific machine they were developed on, the one material it was tested on, or the specific process parameters they were applied to test. Therefore, generalizing these ML models is a current research gap, and more investigation is needed in this area. Moreover, the data, although large in volume, collected from one sensor is sometimes not sufficient. For example, DSLR cameras are only able to detect a large field of view by monitoring layerwise, while high-speed cameras have a narrow field of view and can only detect a small portion of the build plate. Likewise, infrared cameras can detect surface defects, while AE sensors can detect volume defects. Undoubtedly, employing several sensors on the L-PBFs and attempting to fuse the data from these sensors can help enhance detection and classification accuracy. However, new ML algorithms will be needed to cope with the collected data's volume, variety, and velocity.  
Below are some suggested research directions to improve the use of ML techniques for in situ monitoring and control of the L-PBF processes.