Industrial and Manufacturing Generative AI: Bibliographic, Keyword, and Thematic Dataset (2022–2026)
Description
This dataset contains the data used for a systematic review and bibliometric analysis of applications of Generative Artificial Intelligence (GAI) in industrial and manufacturing contexts. The dataset includes 373 peer-reviewed academic sources published between 2022 and 2026 and retrieved from Scopus, Web of Science, and ACM Digital Library. The selected publications focus on GAI-related methods and applications, including generative design, large language models, diffusion models, generative adversarial networks, foundation models, retrieval-augmented generation, knowledge graphs, electronic design automation, additive manufacturing, materials discovery, smart manufacturing, robotics, digital twins, and human–AI collaboration. The dataset provides the final deduplicated bibliographic corpus, normalized author-keyword tables, keyword-normalization thesaurus, thematic coding sheet, binary document–keyword matrix, and co-occurrence-analysis outputs. It supports reproducibility of the bibliometric analysis, keyword normalization, thematic classification, and co-occurrence analysis presented in the review. Main validation values: 373 final records; 340 records with available author keywords; 1,806 normalized keyword instances; 1,070 globally unique normalized keywords; 530 top-20 keyword-frequency occurrences; 528 binary top-20 document–keyword assignments; and 107 non-zero co-occurrence edges.