# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 2 Machine Learning
### Semi-Supervised Learning  
Semi-supervised learning occupies a middle ground between two well-established paradigms: supervised learning, which operates on labeled data samples to establish input-output mappings; and unsupervised learning, which discerns patterns or structures within unlabeled data [79]. In semi-supervised learning, a model is trained using a dataset comprising labeled and unlabeled samples. The labeled data aids in refining predictions, while the unlabeled data assists in developing more robust representations of the underlying data structure, thereby enhancing the model's ability to generalize to new, unseen data. Consequently, the overall performance of the model can be significantly improved. Semi-supervised learning has employed various techniques, such as self-training, co-training, consistency regularization, and pseudo-labeling. These methods harness the labeled data to initialize the model and then utilize it to make predictions on unlabeled data, iteratively refining the model for enhanced performance. Table 3 compares different semi-supervised learning methods based on their primary applications, strengths, and limitations. Self-training, a popular semi-supervised learning method, capitalizes on unlabeled data to enhance model performance [80]. Traditionally, this technique involves using an initially trained model on labeled data to generate pseudo-labels for unlabeled samples, which are then incorporated into the training set to update the model. However, this process can be time-consuming and prone to error accumulation due to incorrect pseudo-labels. Researchers have proposed several alternative solutions to address these challenges, especially mitigating error accumulation during self-training. [PERSON] et al. introduced a method that averages the results of various augmentation techniques to label unlabeled data samples [81]. Other approaches have utilized confidence thresholds to control the quality of label assignments, ensuring that only strongly augmented data samples receive labels [82; 83; 84]. Additionally, curriculum labeling has been suggested as another strategy to refine the quality of pseudo-labels during model iterations [85]. Recently, an incremental self-training technique has been developed to discern the positivity of unlabeled data through clustering [86]. This method processes the model in sequential batches to enhance performance. Moreover, a sequential query list has been introduced to streamline the  
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process by reducing the time consumption associated with multiple clustering and queries in iterative learning. These advancements aim to make self-training more efficient and effective in leveraging unlabeled data for model improvement. Co-training, which originated from self-training, serves as the foundation for the bifurcated method in semi-supervised learning and is readily implementable with most ML algorithms [87; 88; 89]. Initially, co-training algorithms primarily focused on multi-view learning, which involves training multiple models (referred to as learners) on distinct subsets of features or data samples [90; 91]. Each model learns from labeled data and subsequently collaborates with the other models to label the unlabeled data, leveraging inter-model agreements to reduce uncertainty. Consequently, co-training enhances model generalization by fostering cooperation among multiple learners. The key steps in co-training involve view acquisition, learner differentiation, and label confidence estimation. View acquisition necessitates a delicate balance between the independence and sufficiency of split views. Learner differentiation arises from employing basic models, selecting optimization algorithms, and configuring learner parameters (i.e., model parameters). Estimating label confidence is crucial for avoiding incorrect labeling scenarios.