# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 1 Introduction
AI endeavors to engineer systems capable of tasks, typically requiring human intelligence, such as problem-solving, reasoning, and natural language comprehension. ML, a subset of AI, focuses on developing algorithms that enable computers to learn from data without being explicitly programmed. Within ML, supervised and unsupervised learning are prominent paradigms for processing existing data. Supervised ML, encompassing classification and regression, operates on labeled data featuring input features alongside corresponding outputs or targets. Conversely, unsupervised ML tackles unlabeled data, exploring data structure for clustering and dimensionality reduction. Deep learning (DL), a subset of ML, harnesses neural networks with multiple layers to represent transformations and handle complex tasks, such as image recognition, natural language processing, and speech synthesis [30; 31; 32; 33]. This methodology, characterized by its depth, has significantly advanced the field. The ML process entails identifying patterns, correlations, and statistical structures within datasets, empowering the development of intelligent systems for predictive fidelity, decision-making, and task automation. The continuous advancement of AI and ML has catalyzed breakthroughs across diverse domains, including materials science, hydrology, finance, and healthcare [34; 35; 36; 37; 38]. Through a combination of rigorous mathematical modeling, algorithm development, and empirical validation, scientists and engineers persistently expand the frontiers of AI and ML, unlocking new capabilities and potentials for intelligent systems. Reinforcement learning (RL) [39], another vital subset of ML, stands as a fundamental pillar in AI, mirroring how humans learn to navigate and make decisions in a dynamic environment. Unlike supervised and unsupervised learning, RL does not rely on pre-collected datasets. Instead, the RL agent operates within an environment, iteratively exploring and learning from its experiences to achieve a specific goal through trial and error. Central to RL is the concept of reward, which provides positive or negative feedback from the environment after the agent takes action. The RL agent's objective is to learn optimal policies or strategies, guiding its decision-making process to maximize cumulative rewards over time. Reinforcement learning has diverse applications across domains like healthcare and recommendation systems [40; 41]. For example, it has expelled in game-playing tasks, often surpassing human performance in chess, Go, and video games. In robotics, RL algorithms train robots to efficiently perform complex tasks, while in self-driving cars and drones, RL aids in navigation, path-finding, and decision-making [42; 43; 44]. Moreover, RL finds utility in algorithmic trading and portfolio optimization, enhancing risk management and investment decisions [45]. Smart grid systems optimize energy consumption, facilitate demand response, and integrate renewable energy resources [46]. In summary, RL presents promising solutions for addressing complex real-world challenges where explicit instruction or exhaustive search methods are impractical. Reinforcement learning's adaptability and learning capabilities make it a versatile tool for tackling diverse problems across multiple domains. Several reviews have examined the applications of ML in the AM domain [47]. [PERSON] and co-workers provided a thorough overview of supervised and unsupervised ML tasks in AM, focusing on parameter optimization and anomaly detection [48]. They explored regression, classification, and clustering techniques, delving into their roles in enhancing AM processes. In their analysis of regression models, the authors highlighted neural networks and Gaussian process regression as significant tools for parameter optimization, property prediction, and geometric deviation control [49; 50; 51]. Notably, with its probabilistic characteristics, Gaussian process regression offers the ability to quantify uncertainty--an essential feature in AM applications [52].