# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### Technical and Business Analysis of the Usability of Proposed New Technologies  
ML in 3D printing is revolutionizing additive manufacturing by optimizing material usage, reducing waste, and improving print accuracy through predictive analytics [3; 4]. ML algorithms can improve process control by analyzing sensor data from printers in real time, enabling automatic adjustments to mitigate defects such as warpage, underextrusion, or layer shifting [1]. Advanced ML algorithms help enable generative design, where AI-based algorithms create high-performance, lightweight structures optimized for strength and material efficiency, particularly in aerospace and automotive applications [54]. ML enables adaptive slicing techniques that dynamically adjust layer thickness based on complexity, improving surface quality and print speed without compromising structural integrity. ML-based anomaly detection models analyze high-resolution images and thermal data to identify defects during printing, preventing material waste and reducing post-processing costs [55]. ML accelerates material innovation by predicting mechanical properties and optimizing composite formulations for strength, durability, and heat resistance, reducing the need for extensive physical testing [56]. Reinforcement learning algorithms enable autonomous printer calibration, reducing setup time and improving repeatability across machines and materials. Machine learning-based predictive maintenance models analyze historical performance data to predict printer component failures, minimizing downtime and costly repairs. For mass customization, ML enables efficient production planning by optimizing batch scheduling, reducing lead times, and personalizing product designs based on customer preferences [57]. AI-based topology optimization integrates with 3D printing to push the boundaries of traditional design, creating biomimetic structures that maximize efficiency and material performance. Cloud-based ML platforms facilitate distributed 3D printing networks, enabling companies to leverage decentralized manufacturing with real-time monitoring and optimization across multiple locations. ML improves multi-material printing by dynamically adjusting extrusion parameters, ensuring smooth transitions between different materials with minimal defects [58]. Integrating ML with digital twins in 3D printing enables virtual simulations of printing processes, predicting failures before physical production begins, saving costs and time [59]. Companies that use ML in 3D printing gain a competitive advantage by reducing production costs, improving product quality, and accelerating time to market through process automation and optimization [60]. As machine learning advances, its role in 3D printing will expand beyond defect detection and optimization, driving innovation in self-healing materials, AI-driven robot assembly, and fully autonomous digital manufacturing ecosystems [61] (Table 5). ML models vary in performance, strengths, and weaknesses based on the complexity of the problem, data availability, and computational requirements [3; 4; 62; 63]. Decision trees are interpretable and fast but are prone to overfitting, especially for small datasets, while random forests mitigate overfitting by averaging multiple trees, which improves accuracy at the cost of increased complexity. Support vector machines perform well on high-dimensional data and offer robust classification but can be computationally expensive on large datasets. Logistic regression is efficient for binary classification and interpretable but has issues with nonlinearly separable data compared to more complex models. Neural networks are excellent at processing large, unstructured data such as images and text but require long training times and computational resources. K-Nearest Neighbors is simple and effective for small datasets but becomes inefficient as the dataset size grows due to its dependence on distance computations.