The Integration of Additive Manufacturing into Industry 4.0 and Industry 5.0: A Bibliometric Analysis (Trends, Opportunities, and Challenges)
Bibliographic record
Abstract
This bibliographic analysis explores the evolving landscape of additive manufacturing (AM) in the context of Industry 4.0 and the emerging paradigms of Industry 5.0. This research critically examines the key literature and scholarly works to clarify the evolution, challenges, and opportunities presented by integrating AM technologies with digital transformation and advanced industrial practices. The exploration begins by tracing the foundational concepts of Industry 4.0, emphasizing the role of cyber–physical systems, data analytics, and automation in reshaping manufacturing ecosystems. It then moves to the developments of Industry 5.0, focusing on human-centric approaches, collaborative robotics, and sustainable manufacturing strategies that extend beyond automation. The impact of AM technologies across various sectors, from aerospace and automotive industries to healthcare and consumer goods, is central to this analysis. This article synthesizes empirical studies, case analyses, and theoretical frameworks to discern the synergies, challenges, and prospects of integrating AM into Industry 4.0 and the evolving Industry 5.0. Through this bibliographic journey, readers gain insights into the transformative potential of AM as a catalyst for innovation, agility, and sustainability in the digital age. The findings underscore the need for interdisciplinary collaborations, policy frameworks, and technological advancements to harness AM’s full potential within Industry 4.0 and 5.0.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.188 | 0.282 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".