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

Author

Listed:
  • Onuchukwu Godwin Chike

    (Universiti Teknologi Malaysia
    Nigerian Army University Biu)

  • Yee Jian Chin

    (Universiti Teknologi Malaysia)

  • Norhayati Ahmad

    (Universiti Teknologi Malaysia)

  • Wan Fahmin Faiz Wan Ali

    (Universiti Teknologi Malaysia)

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.

Suggested Citation

  • Onuchukwu Godwin Chike & Yee Jian Chin & Norhayati Ahmad & Wan Fahmin Faiz Wan Ali, 2025. "Impact of Machine/Deep Learning on Additive Manufacturing: Publication Trends, Bibliometric Analysis, and Literature Review (2013–2022)," SN Operations Research Forum, Springer, vol. 6(2), pages 1-29, June.
  • Handle: RePEc:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00440-1
    DOI: 10.1007/s43069-025-00440-1
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    References listed on IDEAS

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    1. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    2. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    3. Hellsmark, Hans & Jacobsson, Staffan, 2009. "Opportunities for and limits to Academics as System builders--The case of realizing the potential of gasified biomass in Austria," Energy Policy, Elsevier, vol. 37(12), pages 5597-5611, December.
    4. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    5. Sachin Kumar & T. Gopi & N. Harikeerthana & Munish Kumar Gupta & Vidit Gaur & Grzegorz M. Krolczyk & ChuanSong Wu, 2023. "Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 21-55, January.
    6. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    7. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    8. Quan-Hoang Vuong & Huyen Thanh T. Nguyen & Thanh-Hang Pham & Manh-Toan Ho & Minh-Hoang Nguyen, 2021. "Assessing the ideological homogeneity in entrepreneurial finance research by highly cited publications," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
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