# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Process Modeling and Control  
[PERSON] et al. reviewed both conventional closed-loop control and ML-assisted approaches in metal AM [133]. They highlighted that offline ML models could optimize AM process parameters before the process begins, whereas online models can analyze in situ sensory data to detect and diagnose defects in real-time. Additionally, they proposed an ML-assisted control framework for managing defects and anomalies during AM. [PERSON] et al. also reviewed process optimization and in situ monitoring techniques in LPBF AM, with a particular focus on supervised learning methods [134]. ML tasks in their review include melt pool characterization, mechanical property prediction, quality regression, and classification, among others. Several recent works on applying ML to process modeling and control are summarized in Table 8, with further details provided in this subsection. To mitigate uncertainty in the mechanical properties and quality of parts produced using fused filament fabrication (FFF), [PERSON] et al. developed a DL method to enhance the AM processing system's reliability by optimizing input parameters and predicting system responses [135]. Latin hypercube sampling was employed for experimental design to explore the input feature space and generate data efficiently [136]. The DL framework comprised an RNN and an ANN. The RNN was tasked with estimating a behavior vector based on the input parameters and past observations of the system, while the ANN predicted the system responses. This approach facilitated the identification of significant \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Applications** & **ML Methods** & **Data Collection** \\ \hline \multirow{2}{*}{Surface quality classification [122]} & CNN with pre-trained ResNet50 architecture & 250 images taken by an Olympus optical microscope \\ \hline Surface deformation defect & CNNs with different architectures & 511 images taken before and 511 images after the AM process \\ \hline Failure detecting and grading [124] & CNN with pre-trained Inception-v3 architecture & 5000 images captured during the printing processes \\ \hline Part recognition [125] & CNN with three different pre-trained models & A large dataset generated from CAD models \\ \hline Surface roughness classification [129] & RF, ANN, SVM, and LSTM during printing & Singal data collected a photodiode sensor during printing \\ \hline Porosity defects investigation [130] & Mask region-based CNN [131] & 120 manually annotated CT slices \\ \hline Melt pool anomaly characterization [132] & Convolutional autoencoder & 5000 melt-pool images from the in situ sensing system \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Some studies on supervised learning for quality detection in AM. input parameters crucial for establishing a reliable AM processing system. The principle of PIML was integrated into this DL method [137]. Domain knowledge, expressed through physical laws, was utilized to initialize neural networks to learn fundamental correlations between features. This aspect was essential for the method's applicability across different FFF 3D printing systems. For practical implementation in mass production, the authors addressed the controllability of print bed adhesion. Relevant domain knowledge, including various measurements and datasets, was extracted from scientific literature and incorporated into the proposed neural networks [138]. A case study involved four 3D printers in a temperature- and humidity-controlled environment. Four hundred experiments were designed, resulting in 1273 print bed adhesion measurements, with 20% forming a testing set. Compared to a statistical approach, the proposed method exhibited a significantly lower root mean square error (RMSE), especially when limited measurements were available. Modeling over-deposition is critical in controlling the laser metal deposition (LMD) process to prevent additional materials from melting onto the substrate. [PERSON] et al.