# 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
developed an LSTM model to simulate over-deposition, focusing on nickel alloys with various shapes deposited via LMD [139]. Data collected during and after deposition included deposition head positions, laser activation signals, melt pool images, and deposited shapes. After data fusion, several new input features were incorporated, such as deposition speed and two variables representing the geometry of deposited shapes. Sequences of input features for 20 deposition steps were fed into the LSTM to predict deposition height for the next time step. The relative errors on the testing sets ranged from 6% to 11%. The results underscored the importance of selecting simple geometries to construct the training set for modeling the LMD process. The authors intended to utilize this model in closed-loop control of deposition parameters during real-time processes, indicating its potential for practical application in enhancing LMD processing control and efficiency. [PERSON] and colleagues developed a predictive geometry control framework for jet-based AM 3D printing processes [140]. A key component of this learning and control framework was a physics-guided data-driven model, which utilized a convolutional recurrent neural network (convRNN) to forecast the height evolution of AM manufactured parts across various scenarios [141]. The convRNN model parameters were determined by training the network with data from a limited number of layers during printing. Notably, the model could be represented in dual forms, where the layer droplet input pattern became the network parameters, making the network architecture interpretable in physical terms. This model was integrated into a feedforward control scheme, with experimental findings demonstrating its superiority over state-of-the-art open-loop control methods for 3D printing processes. Furthermore, the researchers developed an online learning algorithm for the convRNN model and implemented it into a feedback control system. They also conducted stability analyses of the developed model predictive control framework using Lyapunov theorems. The physics-guided principle allowed minimal computational effort to update the convRNN model while effectively controlling the printing system in practical applications. Additionally, the online learning and feedback control strategy mitigated process uncertainties and enhanced the geometric accuracy of the printed parts. [PERSON] et al. developed a novel method named multi-fidelity point-cloud neural network (MF PointNN) for surrogate modeling of the melt pool, which is critical for UQ and quality control in metallic AM processes, such as electron beam AM [142]. They generated a high-fidelity (HF) dataset comprising 280 data points using finite element (FE) modeling and simulation under uncertainty. The input features included controllable and uncontrollable parameters, while the output targets represented the thermal field response. Controllable parameters included preheating temperature, electron beam power, and beam velocity. Uncertainty stemmed from uncontrolled parameters treated as random variables, such as the absorption efficiency of beam power and material and thermal properties of titanium alloy powder, including thermal conductivity, specific heat capacity, and density. Moreover, they derived a low-fidelity (LF) dataset of 280 data points from FE simulations with coarser meshes than those used for generating HF data. In the proposedMF PointNN framework, an LF PointNN was initially trained based on the LF training samples. Subsequently, by freezing specific network coefficients of the LF PointNN, a new PointNN was fine-tuned using the HF training data. The efficacy of this method was evaluated by comparing predictions with various methods, including FE analysis, surrogate modeling using kriging and singular value decomposition (SVD), and a standard MF modeling approach using kriging and SVD [143].