# Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review
## 1 Introduction  
Intelligent manufacturing, or smart manufacturing, leverages advanced technologies, such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), robotics, automation, and big data analytics, to revolutionize production processes [1; 2; 3; 4; 5; 6]. In additive manufacturing (AM), ML has transformative applications that enhance productivity, efficiency, and flexibility in production [7]. ML algorithms can drive predictive maintenance by analyzing large datasets generated at every production stage, ensuring equipment reliability and reducing downtime. ML is also pivotal in quality control, detecting defects, and ensuring consistent output in AM processes. Demand forecasting and process optimization further benefit from ML by accurately predicting material requirements, optimizing energy usage, and improving production speed. Integrating IoT devices and sensors across AM setups allows real-time monitoring, enabling ML models to adapt quickly to variations in the process or material properties, thus maintaining product quality [8; 9]. Advanced robotics powered by ML can adjust to complex AM tasks, collaborating with human operators for efficient, precise assembly [10; 11; 12]. Automation in AM, coupled with ML, streamlines workflows, enhances consistency, and minimizes waste, ultimately reducing production costs [13]. These technologies enable intelligent manufacturing environments that promote agility, customization, and sustainability, reshaping traditional manufacturing into a responsive, data-driven ecosystem [14; 15; 16]. Traditional manufacturing methods like milling and turning rely on subtracting materials from a solid bulk to shape a final product [17]. In contrast, AM, often known as 3D printing, builds objects layer by layer by adding materials precisely as needed from digital designs [18; 19]. This layer-by-layer approach enables AM to create complex geometries that are difficult or impossible to achieve with conventional methods, minimizes material waste, and reduces production time, allowing for faster prototyping and customization. Key AM technologies include fused deposition modeling, stereolithography, and selective laser sintering, each offering unique advantages. For instance, fused deposition modeling, one of the most accessible techniques, uses thermoplastic filament to build layers, making it affordable and user-friendly [20]. Stereolithography, on the other hand, utilizes a UV laser to solidify photopolymer resin, yielding highly detailed parts with smooth surface finishes [21]. Selective laser sintering employs a laser to fuse powdered materials, ideal for producing durable, intricate parts from metals and ceramics [22]. Beyond these, advanced methods like direct metal laser sintering, electron beam melting, and binder jetting extend AM's potential to even more demanding applications [23; 24; 25; 26; 27]. AM represents a paradigm shift, promoting resource efficiency and encouraging innovation across various industries, from aerospace and automotive to healthcare and consumer goods. Enabling the synthesis of bio-inspired materials and structures opens avenues for engineering resilient, lightweight designs [28; 29]. However, the scalability of AM, material constraints, and quality control present ongoing challenges that call for further research and development. Nevertheless, the technology's capacity for customization, reduced waste, and rapid production cycles signal a transformative impact on the future of manufacturing. In our view, AM has the potential to redefine sustainable manufacturing practices, aligning with both industrial needs and environmental goals in a way that traditional methods cannot fully achieve. This optimism, tempered by awareness of current limitations, underscores our belief in AM as a catalyst for future-oriented, efficient manufacturing solutions. AI and ML stand at the crossroads of computer science, statistics, mathematics, and cognitive psychology.