# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 3 Applications of Supervised Learning in Additive Manufacturing
### Fatigue Life Prediction  
[PERSON] et al. conducted a literature review focusing on ML-assisted fatigue life prediction of AM metallic materials [104]. They found that most studies on the fatigue life of AM parts utilized ANN models, while random forest (RF) and SVR models were applied less frequently. Additionally, most of the reviewed literature focused on stainless steel, aluminum alloy, and titanium alloy. The authors also discussed challenges and future perspectives, including difficulties in data collection, labeling, and feature engineering. Another review can be found in [105]. Table 6 presents a summary of the reviewed papers focusing on the application of supervised learning techniques for fatigue life prediction. It includes concise descriptions of the AM techniques employed in each study, the ML algorithms utilized for prediction, and the methods of data collection. These summaries aim to provide a clear understanding of the diverse approaches found in the literature. The rest of this section will explore these aspects in greater detail. [PERSON] et al. addressed fatigue and service life prediction in AM products using Support Vector Regression (SVR) [106]. Their study focused on thirty specimens of titanium alloy produced via the laser-directed energy deposition (LDED) method, followed by double-annealing heat treatment. Subsequently, constant-amplitude fatigue tests were conducted across three different stress levels to measure the fatigue lives, which served as labels or outputs. Microstructural analysis, facilitated by an optical microscope and scanning electron microscope (SEM), enabled the examination of the specimens for defects, particularly pores [107]. The input features in their study included the range of stress intensity near pores, pore type, the distance-size ratio (the ratio of the distance from a pore to the free surface to the equivalent diameter of the pore), pore area, and the peak stress applied on the specimen. The range of stress intensity was calculated using [PERSON]'s approach [108], which was instrumental in evaluating stress distribution around pores. Pore type, a categorical feature, categorized pores into four types based on SEM images, considering pore size and facet visibility. Various SVR models were trained using different combinations of input features. It was found that a model incorporating two features, the \begin{table}
\begin{tabular}{c l l} \hline \hline  & **Value-Based** & **Policy-Based** \\ \hline \multirow{4}{*}{Definition} & Focuses on learning a value & \multirow{2}{*}{Directly learns a policy (a mapping} \\  & function (e.g., state value or action value) to estimate the expected & \\  & return of actions of states. & \\ \hline \multirow{4}{*}{Advantages} & Often more stable and sample & \multirow{2}{*}{Can handle continuous action} \\  & efficient, as it uses value functions & \\  & to guide action indirectly. & \\ \hline \multirow{2}{*}{Disadvantages} & May struggle with continuous & \multirow{2}{*}{Can be less sample efficient and} \\  & action spaces and often rely on an & \\ \multirow{2}{*}{exploration strategy.} & \multirow{2}{*}{The goal of the project.} & \multirow{2}{*}{Can handle continuous action} \\  & exploration strategy. & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between value-based and policy-based RL approaches. range of stress intensity and pore type, demonstrated superior performance in terms of errors and correlation coefficients. Additionally, alternative ML models, including ANNs, random forests, and Gaussian process regression, were evaluated. The comparative analysis underscored the effectiveness of the SVR model in accurately predicting fatigue life for the LDED titanium alloy specimens. Using the ML model to predict the fatigue life of AM products requires fabricating and measuring a sufficient number of samples, which is often impractical. Recent studies have proposed incorporating physics knowledge into \"black-box\" ML mode to address this issue.