# Emerging Applications of Machine Learning in 3D Printing
## 2 Materials and Methods  
### Data Set  
This bibliometric analysis aims to investigate the research landscape and the state of knowledge and practice in the area of planning and implementing ML for the optimization of materials and 3D printing technologies. For this purpose, we used bibliometric methods to analyze recently (i.e., up to 10 years back) published scientific publications with a global  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gap(s)** & **Detailed Description** \\ \hline Data scarcity and quality & Limited availability of high-quality, annotated datasets for training ML models, especially for specific materials or complex geometries in 3D printing. \\ \hline \multirow{2}{*}{Material-specific modeling} & \
\begin{tabular}{c} Inadequate ML models tailored to different materials such as composites, ceramics, and biomaterials that exhibit diverse and complex behaviors during printing. \\ \end{tabular} \\ \cline{2-3}  & Complexity of multi-material 3D printing modeling. \\ \hline Generalization across technologies & Difficulty in generalizing ML models across 3D printing technologies due to varying process parameters and constraints. \\ \hline Real-time monitoring and control & Challenges in developing ML systems for real-time monitoring, defect detection, and adaptive control of the 3D printing process, especially for high-speed printing. \\ \hline Standardization and interoperability & Lack of standardized data formats and protocols across 3D printing platforms, limiting the portability and scalability of ML applications. \\ \hline Multi-scale modeling & Gaps in ML integration for multi-scale analysis, linking micro-scale material properties to macro-scale performance of printed parts. \\ \hline Energy consumption and sustainability & Limited use of ML to optimize energy consumption and minimize waste in 3D printing processes, which are critical for sustainable production. \\ \hline Error prediction and mitigation &
\begin{tabular}{c} Inadequate models to predict and mitigate common errors such as warping, layer adhesion issues, and collapse of protrusions during printing. \\ \end{tabular} \\ \hline Integration with computer-aided design (CAD) tools & Poor integration of ML with CAD software to optimize designs specifically for 3D printing, such as topology optimization and support structure design. \\ \hline End-user accessibility & Limited efforts to make ML applications user-friendly and accessible to non-expert operators, hindering widespread adoption in industry and hobbyist markets. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Observed gaps in emerging ML applications in 3D printing (own version).  
reach. The approach used includes formulating research questions to identify key areas that include the current state of research, the origin of publications (institutions, country, funding mode), the most influential authors and articles, and, furthermore, the evolution of research topics. When possible, we tried to identify the sustainability goals related to the publications included in the review. This approach allows for a more comprehensive understanding of current research and industry trends, strategies, research, and business practice based on ML in the development of 3D printing. In light of the paradigms of Industry 4.0 (automation, robotization, technical control throughout the production cycle) and Industry 5.0 (human and environment at the center of attention), it becomes necessary to understand and plan further pro-development activities in this field and strengthen its potential. Interpretation of bibliometric data enriches current discussions and provides a solid foundation for future research.