# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 4 Applications of Semi-Supervised Learning in Additive Manufacturing  
[PERSON] introduced a novel semi-supervised DL approach for automatic quality inspection in AM processes, including selective laser sintering (SLS) [148]. Unlike other automated quality inspection systems that rely solely on fully supervised learning and require large amounts of labeled data or images, the proposed approach harnesses labeled and unlabeled data, thereby reducing manual labeling efforts. In this approach, a CNN was utilized, and the loss function comprised the cross-entropy of the labeled images, the cross-entropy of the pseudo-labeled images (where unlabeled images were assumed to have true labels), and an entropy regularization term representing the probabilities of unlabeled images belonging to the true class [149]. The training procedure involved three steps. Initially, only labeled data were fed into the model, and the CNN weights and biases were iteratively adjusted to minimize the loss function. Next, the output probability of each unlabeled data was predicted, and a margin criterion was applied to assign a weight to the data. Finally, combining labeled data and weighted unlabeled data formed a new training set to update the CNN model. This approach was applied to a dataset for SLS powder bed defect detection. The results demonstrated excellent model performance with an accuracy of 98%, comparable to other state-of-the-art approaches, despite using only 25% of the labeled training data samples [150]. The author successfully applied the proposed approach to other publicly available defect inspection datasets, highlighting its flexible and extensive applicability [151; 152; 153; 154]. Numerous supervised learning endeavors have been undertaken to efficiently monitor the quality of products produced through LPBF, a metal AM technique [155; 156; 157]. [PERSON] et al. proposed a semi-supervised ML approach to minimize the effort required for labeling training data samples to detect overheating in LPBF [158; 159]. For data collection, they utilized a digital camera to capture layer-by-layer monitoring images of the powder bed following laser scanning or powder recoating. The images captured after laser scanning were employed to train the ML model. This model, which featured the DeepLab v3 + network with Xception as its backbone, was designed to classify characteristic appearances at the pixel level. Data augmentation techniques were applied to prevent overfitting and enhance the model's robustness. Subsequently, the classified appearances were correlated with post-process characteristics such as surface roughness, morphology, and tensile strength to ascertain the quality of LPBF products, which were categorized as anomaly-free, exhibiting a lack of fusion, or overheated. The results demonstrated that the trained ML model possessed the capability for defect detection and quality prediction across various geometries of products. The authors suggested extending this approach to other 3D printing processes. Additionally, integrating thermal history data and employing RNNs could further enhance the model's ability to predict quality and confirm the occurrence of overheated defects. Several studies have focused on anomaly detection during AM processes using ML techniques to anticipate flaws or porosity in products [160; 161; 162; 163]. However, many of these approaches overlooked the dynamic nature of the manufacturing process. In a novel approach, [PERSON] and [PERSON] proposed a methodology to construct a data-driven model of the LPBF process dynamics, leveraging high-speed cameras co-axial with the laser to capture real-time process signatures during material fusion [164]. By considering the process dynamics, they framed the problem as utilizing sequences of historical observations (i.e., images), process system states, and control inputs to predict residual error between the predicted and observed states at the current time, termed the dynamic signature. This method involved multiple models.