# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in Selecting Materials for 3D Printing  
Compared to the results obtained using the traditional approach based on artificial neural networks (ANN), the optimization based on more advanced ML algorithms im  
Figure 2: Emerging applications of ML in 3D printing. Colors: green: well defined, orange: partly defined, red: not defined yet.  
proved the calculation speed by up to 1.5 times while maintaining the same print quality, better quality, and reduced mean square error (MSE), and also allowed to identify a set of printing parameters not previously determined by trial and error. This indicates that ML is an effective tool with significant potential for wide application in planning and optimizing material properties in the 3D printing process [14]. Moreover, the use of ML allows for more precise adjustment of raw material recipes, which can lead to a significant reduction in material losses. ML methods also allow for the dynamic adaptation of process parameters in real time, which is crucial in the case of complex and demanding production environments. Further research is needed on the application of low-cost but more computationally efficient solutions for multi-task and multi-material additive manufacturing as the number of materials, raw material recipes, and complex printing processes increases. The development of computational technologies supporting ML can further accelerate the optimization process. ML algorithms are already being used to predict and optimise energy absorption capacity under various processing conditions where print speed and layer height were critical values for achieving maximum energy absorption [15]. In the context of multi-component materials, ML can help identify synergies between components, which is difficult to achieve using traditional methods [16]. In the case of manufacturing critical components, such as aerospace parts, ML can contribute to improved reliability and safety. In addition, ML-based systems can support the prediction of the durability of printed elements, which allows for more accurate product life cycle planning. An important application of ML in the sustainable industry within the Industry 5.0 paradigm could be to optimize the mechanical properties of partially recycled 3D printing materials. In the case of recycled polylactic acid (PLA), ML has made it possible to predict the tensile behavior of 3D printing with an average error of 6.059%, and specific combinations of processing and post-treatment annealing parameters improve mechanical properties (e.g., by 7.31% in final tensile strength). Recycled PLA, under certain conditions, can be an alternative to virgin PLA (e.g., in the area of biodegradable packaging) [17]. An integrated approach, combining ML with other data analysis methods, such as evolutionary algorithms, can open up new possibilities in designing materials with required properties. With the development of 4D printing, ML is also becoming a tool enabling the effective design of active materials that respond to external stimuli. However, implementing these technologies in an industrial environment requires appropriate scaling and adapting models to the specific requirements of end users. The use of ML in 3D printing contributes to increasing the sustainability of production processes by reducing waste and optimally using resources [18].