# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in 3D Printing Process Optimization  
The rapidly increasing cost and consumption of plastics underscores the need to optimize 3D printing processes. This can be achieved using advanced ML-based design methods or injection molding simulations [30; 31]. These approaches help save material by reducing filament consumption, minimizing waste, and reducing the environmental impact of the printing process. AI-based optimization saves time and money without compromising the amount of data.  
ing the quality of the printed product. Such improvements offer significant competitive advantages, especially for thin-designed, mass-produced items [32]. By implementing ML, manufacturers can predict optimal printing parameters, ensuring consistency and reducing trial-and-error iterations. AI-based systems can identify inefficient material usage, leading to an estimated one free print for every 6.67 prints from previously wasted material. These savings not only reduce production costs but also align with sustainability goals. Additionally, optimization processes can dynamically adapt to different materials, delivering optimal results regardless of filament type [33]. This flexibility is key to meeting the diverse needs of the industry while remaining cost-effective. ML algorithms also help identify potential defects during the design phase, reducing the likelihood of failed prints [34]. Integrating AI into 3D printing supports more accurate layer deposition, increasing the structural integrity of products. By simulating the printing process, AI can identify the best configurations to minimize support structures, which further reduces material waste. These advances make 3D printing more accessible and environmentally friendly, promoting widespread adoption across industries. In addition to its environmental benefits, optimized 3D printing provides faster production cycles, allowing innovative designs to be brought to market faster. AI-based strategies not only increase efficiency but also increase the overall sustainability of additive manufacturing processes. Even such advanced 3D printing methods as pneumatic 3D printing of nanomaterials, useful for processing advanced materials (e.g., MXene) used in nanoenergetics, flexible electronics, and sensors are subject to process optimization. The pre-assessment evaluates whether the selected process parameters produce uniform, high-quality prints, and then the XGBoost algorithm performs a print quality classification with an accuracy of 90.44%, which facilitates the selection of optimal printing parameters [35].  
The challenge in this area is not only a large number of materials with varying properties subjected to 3D printing with different parameters but also the diverse requirements resulting from their applications, often requiring dedicated solutions. Thus, a large number of data and often conflicting requirements will create demands for the use of ML to analyze them and search for efficient (e.g., energy, material) solutions. ML solutions in this area will need to be versatile and flexible if they are to be cost-effective [36; 37]. A pro-quality issue strongly linked to the Industry 4.0 paradigm is the detection of errors as early as possible in all stages of production, preventing the waste of materials and energy and printing time. This requires the monitoring of the 3D printing process (including vision [38]) and its parameters and a proactive approach to optimize parameters in order to stop at an early enough stage a print that cannot be improved [39; 40].