# Emerging Applications of Machine Learning in 3D Printing
## 2 Materials and Methods
### Methods  
In this study, we searched four bibliographic databases: Web of Science (WoS), Scopus, PubMed, and dblp. Their selection was dictated by their wide range of studies and rich data of global relevance (Table 2). We applied filters to focus on appropriately selected literature, narrowing the search scope to articles in English. After filtering, we manually reviewed each article to ensure that it met the inclusion criteria, which helped determine the final sample size. We then analyzed the main features of the dataset, including the most common authors, research groups/institutions, countries, topic groups, and emerging trends. This allowed us to map key terminology and its evolution, as well as the main research developments in the study area. Where possible, we tracked temporal trends to monitor changes in the research area over time, and we grouped publications into topic clusters, which showed the relationships between different research areas. This process highlighted important themes and subfields within the research area. The study was based on selected elements of the PRISMA 2020 guidelines for bibliographic reviews [8], focusing on the following aspects: justification (item 3), objectives (item 4), eligibility criteria (item 5), information sources (item 6), search strategy (item 7), selection process (item 8), data collection process (item 9), synthesis methods (item 13a), synthesis results (item 20b), and discussion (item 23a) (see Supplementary Materials for details). For bibliometric analysis, tools embedded in the Web of Science (WoS), Scopus, PubMed, and dblp databases were used. This selected review methodology supports bibliometric and scientometric studies, often allowing for refined categorization by concepts, research areas, authors, documents, and sources. The results are presented in a table that \begin{table}
\begin{tabular}{c c} \hline \hline
**Stage Name** & **Tasks** \\ \hline Defining research objectives & Defining goals of the bibliometric analysis \\ \hline \multirow{2}{*}{Selecting databases and data collections} & Choosing appropriate data set(s) and developing research queries \\  & according to the study goals \\ \hline Data preprocessing & Cleaning the collected date to remove duplicates and irrelevant records \\ \hline Bibliometric software selection & Choosing suitable bibliometric software tool for analysis \\ \hline Data analysis & Description, author, journal, area, topics, institution, country, etc. \\ \hline Visualization & Visualizing the analysis results to present insights \\ (where possible) & \\ \hline Interpretation & Interpreting findings in the context of the research goals \\ and discussion & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bibliometric analysis procedure (own approach). allows for flexible analysis and visualization options. Considering the interdisciplinary scope and complexity of the topic, we have summarized the most important results of the review in a summary table. The literature review process begins with a systematic search of multiple databases using predefined keywords and Boolean operators to ensure comprehensive coverage of relevant studies. Inclusion and exclusion criteria are established based on study relevance, publication date, methodological rigor, and relevance to research objectives, ensuring focused and high-quality selection. An initial automated filtering process removes duplicates and clearly irrelevant studies, followed by manual screening of titles and abstracts to further refine the selection. Full-text mining is performed by multiple reviewers to minimize subjective bias, and discrepancies are resolved through discussion or consultation with a third reviewer. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart is used to visually document the number of studies included and excluded at each stage, which increases transparency.