# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Fatigue Life Prediction
Consequently, yield stress, which exhibited a positive correlation with fatigue life, and fatigue stress, which demonstrated a negative correlation, were selected as the input features. Following the training and evaluation of those four regression models, the authors observed that all models demonstrated excellent predictive capabilities. MLR displayed superior predictive performance at a 95% confidence interval, followed by the SVR model. Furthermore, the study addressed hyperparameter tuning and generalization for each model. Specifically, for the ANN model, three or four neurons were recommended for the first hidden layer. For the SVR model, a coefficient of 0.0001 for the RBF kernel function and a regularization hyperparameter of 30 were suggested. It was also recommended that the RF model utilize three estimators with a maximum tree depth of seven. Recently, [PERSON] and co-workers proposed a physics-informed, data-driven framework to predict the nondestructive fatigue life of laser powder bed fusion (LPBF)-processed metal parts [118]. They fabricated two sets of LPBF Ti-6 Al-4V Grade 5 parts for defect characterization and fatigue testing, respectively. Various factors influencing fatigue performance were considered, including process parameters, X-ray computed tomography (XCT)-inspected defects, and fatigue test conditions. To address the challenges of time-consuming fatigue testing and the limited availability of fatigue data, they developed a multimodal transfer learning approach to investigate the relationships between process parameters, defects, and fatigue performance. In the source task, a hierarchical graph convolutional network (HGCN) was employed to classify defects based on process parameters and defect features in images. The feature embeddings learned from the HGCN were then transferred to a neural network layer for fatigue life modeling. The proposed framework was validated through numerical simulations and an experimental case study. The FI score of defect classification reached 0.96, while the fatigue life prediction yielded a mean absolute log error as low as 0.04. In another recent work, [PERSON] et al. employed ML methods to model the fatigue lifetimes in 3D-printed biomaterials [119]. They used fused deposition modeling (FDM), a popular 3D printing method, to fabricate 162 dog-bone samples for their experiments. The study investigated the influence of three key printing parameters on fatigue behavior: print speed, nozzle diameter, and nozzle temperature. By systematically varying these parameters, they aimed to understand how each factor affects the mechanical performance of the printed biomaterials. The authors conducted rotating bending fatigue tests on the samples to gather data, generating an experimental dataset for analysis. In addition to using traditional ML models such as RF and SVR, the authors incorporated extreme gradient boosting (XGBoost), a more advanced ML algorithm known for its high accuracy and efficiency. To enhance model interpretability, they employed Shapley additive explanation (SHAP), a technique allowing more precise insights into how each feature influences the model's predictions. Upon training and testing various ML methods, they found that XGBoost outperformed the others, achieving an impressive R2 score of 0.9766. Furthermore,the SHAP analysis revealed that nozzle diameter had the most significant impact on fatigue lifetimes, suggesting that this parameter plays a critical role in the mechanical durability of the printed biomaterials. Conversely, print speed was identified as the least influential factor. These findings highlight the potential of ML-assisted modeling in optimizing the 3D printing process for enhanced material performance.