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Emerging Technologies and Sustainability Integration in Industry 4.0 Manufacturing: A Bibliometric Analysis Arya Sena; Latief Syahdika; Rafli Ramadhan; Rafli Aulia
RING ME Vol 6 No 1 (2026): RING Mechanical Engineering
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/rme.v6i1.25343

Abstract

The advancement of Industry 4.0 technologies has accelerated the transformation of manufacturing systems toward intelligent, interconnected, and sustainability-oriented industrial environments. Although numerous studies have explored smart manufacturing and digital transformation, limited bibliometric research has comprehensively examined the integration of intelligent manufacturing technologies with sustainability-driven production systems in the post-pandemic industrial era. This study aims to analyze the scientific development, intellectual structure, thematic evolution, and emerging research trends in smart and sustainable manufacturing research within the Industry 4.0 context. A bibliometric analysis was conducted using 616 English-language journal articles indexed in the Scopus database during the 2020– 2025 period. Data were analyzed using Biblioshiny and VOSviewer to evaluate publication trends, country productivity, thematic structures, keyword co-occurrence networks, and topic evolution. The results reveal a substantial increase in research output after 2023, indicating growing global attention toward intelligent and sustainable industrial transformation. China becomes the most productive country, while smart manufacturing, Industry 4.0, and sustainable development were identified as the dominant research themes. The analysis demonstrates strong interconnections among artificial intelligence, machine learning, predictive maintenance, energy efficiency, and sustainable production systems. Emerging topics such as Industry 5.0, green manufacturing, carbon emission reduction, and green economy indicate a transition from automation-oriented manufacturing toward intelligent, human-centric, and environmentally sustainable industrial ecosystems. This study contributes to the literature by providing an updated bibliometric of the convergence between intelligent technologies and sustainability-oriented manufacturing research. The findings offer valuable insights for researchers, industrial practitioners, and policymakers in identifying future research directions and supporting sustainable industrial transformation strategies.
Analisis Bibliometrik dan Tren Penelitian Digital Twin untuk Pemeliharaan Prediktif pada Sistem Fotovoltaik Bifacial Yuhani Yuhani; Ibnu Sina Al Farisi; Albin Putra Surbakti; Rika Romatona; Andi Ramadhan; Arya Sena
JURNAL SIMETRIK Vol 16 No 1 (2026): Jurnal Simetrik (Sipil, Mesin, Listrik)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31959/js.v16i1.3899

Abstract

The rapid development of renewable energy systems has accelerated research on the integration of digital technologies in photovoltaic (PV) systems, particularly in predictive maintenance and intelligent monitoring. This study aims to analyze the research trends, thematic structure, and future research opportunities related to digital twin applications in predictive maintenance for photovoltaic systems using a bibliometric approach. Data were collected from the Scopus database using the query: (“digital twin” AND (“predictive maintenance” OR “fault detection” OR “condition monitoring”) AND (“photovoltaic” OR “solar energy” OR “PV system” OR “bifacial photovoltaic” OR “bifacial PV”)). The study analyzed 60 selected documents published between 2020 and 2026 using the PRISMA approach and VOSviewer visualization. The results indicate a significant increase in scientific publications after 2023, from 1 publication in 2020, 2 in 2021, 2 in 2022, and 4 in 2023, to 13 publications in 2024 and a peak of 32 publications in 2025. The network visualization analysis revealed that the dominant research topics were digital twin, photovoltaic systems, predictive maintenance, fault detection, machine learning, artificial intelligence, and renewable energy. Overlay visualization further showed a research shift from conventional fault diagnosis toward intelligent real-time monitoring systems based on AI, IoT, and predictive analytics. In addition, the bibliometric mapping demonstrated that bifacial photovoltaic technology has not yet emerged as a dominant research cluster or keyword in the existing literature network. This finding indicates that the integration of digital twin and predictive maintenance into bifacial photovoltaic systems remains underexplored and represents a promising future research direction. This study contributes to identifying the evolution of research trends and the emerging opportunities for developing intelligent and sustainable photovoltaic systems based on digital twin technology. Keywords: Digital Twin, Predictive Maintenance, Photovoltaic Systems, Bibliometric Analysis, Renewable Energy, Bifacial Photovoltaic.