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<p>Research articles from the most prominent and popular SCOPUS database that are attributed to the Machine Learning (ML) incorporated manufacturing techniques are collected in this research. This review focuses on hybrid bibliometric analysis in the area of various ML integrated manufacturing process optimization by examining 6,480 documents published in the last decade from the year 2015 to 2025. The hybrid nature of this analysis lies in the integration of two complementary bibliometric approaches, the first one is SCOPUS analyzer used to obtain statistical and relational data and the second one is the Visualization of Similarities (VOS) viewer to obtain network-based analyses including co-authorship, co-occurrence, citation analysis and bibliographic coupling. This combined approach provides both quantitative and qualitative insights, offering a comprehensive understanding of the research landscape. The proposed review visualizes and estimates the contributions of the ML adoption and research developments over last decade. From the statistical analysis and network analysis, it is concluded that USA is the top contributor, followed by China and UK in terms of publications. Based on co-authorship and co-occurrence analysis with&#xa0;VOS viewer, this review identifies the most productive articles with the most productive authors, along with its affiliations, keywords, trends, developments, and future directions.</p>

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Trends, Advancements in Manufacturing Field Using Machine Learning: A Hybrid Bibliometric Analysis on SCOPUS Database

  • 期刊论文

  • 发表日期：

    2026年5月2日

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  • Abdul Arif,
  • Ponugoti Gangadhara Rao,
  • Kalapala Prasad

摘要

Research articles from the most prominent and popular SCOPUS database that are attributed to the Machine Learning (ML) incorporated manufacturing techniques are collected in this research. This review focuses on hybrid bibliometric analysis in the area of various ML integrated manufacturing process optimization by examining 6,480 documents published in the last decade from the year 2015 to 2025. The hybrid nature of this analysis lies in the integration of two complementary bibliometric approaches, the first one is SCOPUS analyzer used to obtain statistical and relational data and the second one is the Visualization of Similarities (VOS) viewer to obtain network-based analyses including co-authorship, co-occurrence, citation analysis and bibliographic coupling. This combined approach provides both quantitative and qualitative insights, offering a comprehensive understanding of the research landscape. The proposed review visualizes and estimates the contributions of the ML adoption and research developments over last decade. From the statistical analysis and network analysis, it is concluded that USA is the top contributor, followed by China and UK in terms of publications. Based on co-authorship and co-occurrence analysis with VOS viewer, this review identifies the most productive articles with the most productive authors, along with its affiliations, keywords, trends, developments, and future directions.