# 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
The results demonstrated that the proposed method enhanced the prediction performance of 3D thermal fields while utilizing a limited number of training data samples, thereby reducing the computation cost associated with FE analysis. [PERSON] and co-workers investigated the stability of the cladding layer's formation during the wire arc additive manufacturing (WAAM) process, significantly influencing the final dimensional precision of WAAM weldments [144]. The experiments utilized the cold metal transfer process, examining four different welding gun offsets and a no-offset setup. Each experiment generates a dataset of 750 samples comprising temperature distribution images on the weldment sidewall as inputs. The objective was to develop a DL model for cladding layer offset recognition, constituting a multi-class classification problem. The authors employed a CNN, integrating identity maps to facilitate the training of exceptionally deep networks. The overall detection accuracy achieved an impressive 99.84%. Most recently, [PERSON] et al. utilized experimental data from the National Institute of Standards and Technology (NIST) to develop an ML model that predicts the melt pool for future parts [145]. They employed a CNN to process raw melt pool images and extract high-quality data, which was then used to train an MLP in creating a data-driven melt pool model. This model can be applied to enable reliable melt pool-guided optimization in AM processes and outperform the existing neighboring effect modeling (NBEM) method regarding average relative error in predicting melt pool size. [PERSON] et al. applied several ML models to predict compositional changes during the AM process [146], using an explainable AI technique to reveal the relationships between process conditions and these compositional changes. Additionally, [PERSON] et al. developed an efficient tool to predict melt pool temperature variations [147]. They defined physics-based features and collected 17,892 data samples from a two-color coaxial photodiode system, employing RF and XGBoost, of which XGBoost achieved an average coefficient of determination score of 0.65. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Applications** & \begin{tabular}{c} **AM** \\ **Techniques** \\ \end{tabular} & \begin{tabular}{c} **ML/Control** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **Data Collection** \\ \end{tabular} \\ \hline Process parameter optimization [135] & FFF 1 & RNN, ANN, and PIML & \begin{tabular}{c} 1273 print bed adhesion \\ measurements from experiments \\ \end{tabular} \\ \hline Over-deposition modeling [139] & LMD & LSTM & Data collected during the \\  & & & deposition of 36 tracks \\ \hline Predictive geometry control [140] & Jet-based AM & convRNN 2 and MPC 3 & Online learning \\ \hline Melt pool modeling [142] & EBM & MF PointNN 4 & Data generated from finite element simulations \\ \hline Cladding layer offset recognition [144] & WAAM 5 & CNN & \
\begin{tabular}{c} 750 temperature distribution \\ images from experiments \\ \end{tabular} \\ \hline Melt pool prediction [145] & Metal AM & CNN and MLP & Experimental data from NIST \\ \hline Compositional change prediction [146] & LDED & ANN, RF, SVM, etc. & 117 ferromanganese specimens \\ \hline Melt pool temperature variation prediction [147] & LPBF & RF and XGBoost &
\begin{tabular}{c} 17,892 data samples collected from \\ a photodiode system \\ \end{tabular} \\ \hline \hline \end{tabular}  
* 1 FF: laser-directed energy deposition; 2 convRNN: convolutional recurrent neural network; 3 MPC: model predictive control; 4 MF PointNN: multi-fidelity point-cloud neural network; 5 WAAM: wire arc additive manufacturing.  
\end{table} 
Table 8: Summary of reviewed papers on supervised learning for process modeling and control in AM.