# Additive Manufacturing and Chemical Engineering: Looking for Synergies from a Bibliometric Study
## 2 Data Sources and Methodology  
The Scopus database was used, which is one of the most comprehensive and up-to-date sources of information on scientific publications. It is recognized as the largest abstract and citation database of peer-reviewed literature, covering more than 27,000 scientific journals and 249,000 books [16]. The following search string was used to obtain the bibliography used in this work: \"(TITLE-ABS-KEY (\"Additive Manufacturing\") AND PUBYEAR \(<\) 2023 AND (LIMIT-TO (SUBJAREA, \"CENG\"))\". This string retrieves documents containing the exact phrase \"Additive Manufacturing\" in the title, abstract, or keywords, published no later than 2022 (the last full year when the search was performed in September 2023), and belonging to the Chemical Engineering subject area. The search returned a total of 3761 documents. From Scopus, the filter count option and complete database were exported into CSV and RIS files, which were used to analyze the search results. The files contained fields such as title, author, year, journal, citations, country, affiliation, language, document type, subject area, and keywords for each document. An Excel file was used to create a database with these fields to organize the data according to the criteria of interest. The developed methodology has two parts: bibliometric performance analysis and bibliometric network analysis or scientific mapping. First, the performance analysis was based on bibliometric indicators that summarize the main characteristics of the scientific documents. This part used the created Excel file. Second, a conceptual bibliometric network analysis was performed based on a co-word network using SciMAT [17]. Although there are several software tools for scientific mapping analysis, SciMAT was chosen because it supports analysis from different perspectives, mainly strategic and dynamic diagrams, and complete cluster networks. The co-word analysis yielded related groups of keywords called clusters. These clusters were characterized by two measures: density, which indicates the internal cohesion of the keywords comprising them, and centrality, which reflects the interaction of a topic with other topics and its relevance in the scientific field studied [18]. A strategic diagram is a two-dimensional space in which clusters are located according to their centrality and density, plotted on the x-axis and y-axis, respectively (Figure 2). In accordance with their position within the strategic diagram, the clusters can be classified into four distinct categories:  
* Motor clusters: Situated in the upper right quadrant and pertain to well-developed and pivotal subjects within the scientific field, exhibiting robust centrality and high density. * Highly developed and isolated clusters: Located in the upper left quadrant and correspond to topics that have marginal importance in the scientific field. These topics are characterized by being highly specialized and peripheral. * Emerging or declining clusters: Located in the lower left quadrant and correspond to very undeveloped and marginal topics or to very recent topics having low density and centrality. * Basic and transversal clusters: Located in the lower right quadrant and correspond to important topics that are not well developed. In a cluster, the related keywords and their interconnections form thematic networks, each of which is labeled with the most significant keyword of the associated theme, which is represented in the center of the cluster (central node). The volume of the nodes is proportional to the number of documents associated with the keywords of the cluster, while the thickness of the lines between keywords is proportional to their equivalence index (directly related to the co-occurrence of both keywords). In the dynamic diagram, thematic areas are defined as a group of clusters that have evolved over different successive time periods. Depending on the interconnections between clusters, a cluster may belong to two or more thematic areas or none at all.