# Emerging Applications of Machine Learning in 3D Printing
## 3 Results  
### Data Sources  
To refine the search, advanced filtered queries were used, limiting the results to articles in English. Searches were performed as follows: in WoS using the \"Subject\" field (consisting of title, abstract, keywords plus and other keywords); in Scopus using article title, abstract, and keywords; and in PubMed and dblp using manual sets of keywords. The databases were searched for articles using keywords such as \"machine learning\", \"3D print\" or \"3D printing\" and \"optimization\" or \"optimisation\" (Table 3).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Parameter** & **Description** \\ \hline Inclusion criteria & Books (and chapters in books), articles (original, reviews, communication, editorials), and conference proceedings in English \\ \hline Exclusion criteria & Books older than 10 years, letters, conference abstracts without full text, other languages than English \\ \hline Keywords used & Deep learning, energy optimization/optimisation, smart city \\ \hline Used field codes (WoS) & “Subject” field (consisting of title, abstract, keyword plus, and other keywords) \\ \hline Used field codes (Sopus) & Article title, abstract, and keywords \\ \hline Used field codes (PubMed) & Manually \\ \hline Used field codes (dblp) & Manually \\ \hline Boolean operators used & Yes, e.g., “3D print” AND (“optimization” OR “optimisation”) AND “machine learning” \\ \hline Applied filters & Results refined by publication year, document type (e.g., articles, reviews), and subject area (e.g., industry, engineering) \\ \hline Iteration and validation options & Query run iteratively, refinement based on the results, and validation by ensuring relevant publications appear among the top hits \\ \hline Leverage truncation and wildcards used & Used symbols like * for word variations (e.g., “3D print” for “3D print” or “3D printing”) and? for alternative spellings (e.g., “optimi?ation”) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed database search query (own version).  
The selected set of publications was then further refined (Figure 1) by manually re-screening articles and removing irrelevant publications and duplicates to determine the final sample size.  
The summary of the results of the bibliographic analysis is presented in Table 4. The review included 19 articles published in the last five years (no older ones were included).  
\begin{table}
\begin{tabular}{l l} \hline \multicolumn{1}{c}{**Parameter/Feature**} & \multicolumn{1}{c}{**Value**} \\ \hline Leading types of publication & Article (87.10\%), proceeding paper (9.68\%), review (3.23\%) \\ \hline Leading areas of science & Materials science multidisciplinary (70.97\%), engineering manufacturing (58.07\%), computer science artificial intelligence (6.45\%), robotics (6.45\%) \\ \hline Leading topics & Nanofibers, scaffolds and fabrication, mechanics, computer vision and graphics \\ \hline Leading countries & USA, Turkey, China, England, Spain, Singapore \\ \hline Leading scientists & None \\ \hline Leading affiliations & Pennsylvania State University, Carnegie Mellon University \\ \hline Leading funders (where information available) & National Natural Science Foundation of China, European Union \\ \hline Sustainable development goals & Industry innovation and infrastructure, good health and well-being, qualityeducation, climate action \\ \hline \end{tabular}
\end{table}
Table 4: Summary of bibliographic analysis results (WoS, Scopus, PubMed, dblp).  
Figure 1: PRISMA flow diagram of the review process using selected PRISMA 2020 guidelines.