# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 1 Introduction
The authors discussed popular ML methods like decision trees, support vector machines (SVM), and convolutional neural networks (CNN) for classification tasks related to quality assessment, quality prediction, and defect detection [53; 54; 55]. They also addressed challenges such as model overfitting and proposed solutions to ensure robust performance. Moreover, the authors tackled the issue of dataset size limitations in AM by exploring clustering analysis methods such as the self-organizing map (SOM) model and the least absolute shrinkage and selection operator (LASSO) model. These techniques can effectively handle datasets with constrained sizes, a common challenge in AM research. Overall, this work offered valuable guidance for defeating ML applications, understanding different ML tasks, and selecting appropriate ML models in the AM domain. By synthesizing insights from various ML approaches, their review contributes to advancing the integration of ML techniques in AM processes. In another comprehensive review, [PERSON] et al. focused on the applications of ML and data mining techniques in AM design, processes, and production control [56]. They initially summarized the digitization in manufacturing within the framework of Industry 4.0, which encompasses smart factories, cyber-physical systems, IoT, and AI [57; 58]. Then, the authors provided an overview of supervised learning, including Bayesian networks, artificial neural networks (ANN), ensemble methods, and CNN. Additionally, they highlighted the role of generative adversarial networks (GAN) alongside CNN in assisting topology design with optimal structures [59]. Modern ML approaches have been employed for synthesizing metamaterials for material design in AM [60]. For AM processes, they reviewed various works utilizing SVM in process parameter optimization, long short-term memory (LSTM) in process monitoring, CNN in geometric deviation control, and LASSO in cost estimation, as well as other works in quality prediction, defects assessment, and closed-loop control [61; 62; 63]. They also outlined the applications of ML in AM planning, quality control, and printability and dimensional deviation management [64; 65; 66]. Additionally, the authors addressed the unique challenge of data security in AM production. They discussed uncertainty in AM through experiment-based uncertainty quantification (UQ) of the AM process, melting pool, and solidification [67; 68]. In their conclusion, the authors emphasized the integration of AM and ML as a pivotal innovation in the context of the fourth industrial revolution. The reviews mentioned above primarily concentrated on one subset of ML, supervised learning, and its applications to advance AM processes and production. However, they overlooked semi-supervised learning and RL, other crucial ML subsets known for their advantages in data mining and optimizing and controlling autonomous systems. Furthermore, the rapid accumulation of literature publications in the AM domain warrants an exploration of more recent pioneering studies. Hence, this study will present a state-of-the-art review of ML in the AM domain, emphasizing recent groundbreaking research and the applications of semi-supervised learning and RL. The structure of this paper is outlined below: Section 2 describes ML techniques. Subsequently, recent pioneering applications of ML and RL in AM are individually reviewed, culminating in the conclusion.