# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Quality Detection  
[PERSON] and [PERSON] reviewed the application of AI and ML techniques to improve quality assurance in AM processes [120]. Their review focused on the use of CNN for defect detection, highlighting CNNs' capabilities in analyzing large volumes of image data to identify and classify defects in AM products accurately. They noted that CNNs are particularly effective in this domain due to their ability to learn complex patterns and features, which makes it possible to detect various defect types with high precision. In addition, [PERSON] et al. examined an advanced two-stage CNN method, Faster R-CNN, which has been effectively employed for defect localization and segmentation in AM components [121]. We reviewed several studies on supervised learning for quality detection in AM, which are summarized in Table 7, followed by detailed descriptions. Convolutional neural networks have become a standard tool for assessing the quality of parts produced through AM, particularly in scenarios where surface polishing post-processing affects wear resistance and residual stresses. [PERSON] and [PERSON] integrated CNN classification into the process of electrical discharge-assisted postprocessing to enhance the surface quality of AM components [122]. In their study, titanium alloys were prepared using a direct metal laser sintering 3D printer. The researchers selected the lowest discharge energy regime for polishing the samples. Images captured through an optical microscope were fed into a CNN architecture comprising five convolutional layers and a fully connected layer for prediction. This task was a multiclass classification problem, with five surface categories reflecting different levels of surface roughness. Each class had 50 data samples (images) manufactured and polished under distinct printing and process parameters. The study leveraged the pre-trained ResNet50 model to expedite model training. Subsequently, the convolutional layer weights were transferred to the proposed CNN model, with only the weights of the fully connected layer and the output layer being fine-tuned. The researchers employed a fivefold cross-validation approach during the model training, achieving an impressive overall accuracy of 96%. Notably, all \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Applications** & **AM Techniques** & **ML Methods** & **Data Collection** \\ \hline Fatigue and service life prediction [106] & LDED 1 & SVR models with different input features & The authors examined 30 specimens and conducted fatigue tests [107] \\ \hline Fatigue life of defective materials [109] & SLM 2 & Physics-informed neural network & Five series of samples are made and tested [110] \\ \hline Fatigue crack growth life prediction [111] & HRAM 3, LDED, and LMD 4 & SVR, ANN, and Bayesian optimization & Three datasets from [104; 114; 115] \\ \hline Fatigue life prediction [116] & EBM 5 & MLR 6, ANNs, SVR, and RF & Experimental data from [117] \\ \hline Nondestructive fatigue life [118] & LPBF 7 & HGCN 8, transfer learning & Two sets of parts were fabricated for testing \\ \hline Fatigue lifetime modeling [119] & FDM 9 & XGBoost 10, RF, SVR & 162 samples were made for fatigue testing \\ \hline \hline \end{tabular}  
* \({}^{1}\) LDED: laser-directed energy deposition; \({}^{2}\) SLM: selective laser melting; \({}^{3}\) HRAM: hybrid in situ rolled wire + arc additive manufacturing; \({}^{4}\) LMD: laser melting deposition; \({}^{5}\) EBM: electron beam melting; \({}^{6}\) MLR: multiple linear regression; \({}^{7}\) LPBF: laser powder bed fusion; \({}^{8}\) HGCN: hierarchical graph convolutional network; \({}^{9}\) FDM: fused deposition modeling; \({}^{10}\) XGBoost: extreme gradient boosting.  
\end{table} 
Table 6: Summary of reviewed papers on supervised learning for fatigue life prediction in AM. false negative predictions were confined to borderline classes, indicating the robustness of the CNN model in discerning subtle differences in surface quality. In a separate study, [PERSON] et al.