Impact of Machine/Deep Learning on Additive Manufacturing: Publication Trends, Bibliometric Analysis, and Literature Review (2013–2022)

Onuchukwu Godwin Chike (), Yee Jian Chin, Norhayati Ahmad and Wan Fahmin Faiz Wan Ali
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Onuchukwu Godwin Chike: Universiti Teknologi Malaysia
Yee Jian Chin: Universiti Teknologi Malaysia
Norhayati Ahmad: Universiti Teknologi Malaysia
Wan Fahmin Faiz Wan Ali: Universiti Teknologi Malaysia

SN Operations Research Forum, 2025, vol. 6, issue 2, 1-29

Abstract: Abstract This paper critically examined the research landscape and the impact of machine learning (ML) on additive manufacturing (AM) through publication trends, bibliometric analyses, and literature review. The Elsevier Scopus database was selected to identify and recover publications on ML in AM research published from 2013 to 2022 based on the PRISMA approach. The recovered bibliographic data was analyzed using VOSviewer software to examine the co-authorship, keyword, and citation networks on the ML in AM research. Results showed that the publications output (and citations count) increased progressively from 1 (19) to 375 (980) from 2013 to 2022, which exhibits the high total citation to total publication ratio typically characteristic of highly impactful fields with future growth potentials. Analysis of top performers on the topic revealed that Prahalada K. Rao (USA), Pennsylvania State University (USA), and National Science Foundation (USA) are the most prolific authors, affiliations, and funder of ML in AM research, respectively. Hence, the most active nation on ML in AM research is the USA, although China and the UK have also made significant contributions over the years. Keyword occurrence revealed the existence of several research hotspots with researchers’ interests directed at basic research, optimization studies, industrial applications, and novel learning systems. The paper showed that ML in AM is a broad, complex, and impactful research area that will continue to experience scientific growth and technological development, mainly due to the growing demands for accurate computational methods for AM prototypes, processes, and products.

Keywords: Machine learning; Additive manufacturing; Bibliometrics; Literature review; Scopus database (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00440-1

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