Tungsten Inert Gas Welding Research Trends: A 60-Year Bibliometric Analysis Using Vosviewer and Biblioshiny
Bibliographic record
Abstract
Abstract Tungsten Inert Gas (TIG) welding constitutes a key process in the fabrication of welded structures, with widespread application across various sectors of modern industry. It continues to be the subject of extensive research due to its technical advantages and versatility. However, despite its industrial importance, TIG welding has not yet been the focus of a comprehensive bibliographic review. Therefore, the objective of this study is not only to present the current state of knowledge but also to identify key process directions and emerging research trends through a bibliometric analysis of 8,789 publications indexed in Web of Science. The analyses were performed mainly in VOSviewer 1.6.20 and Biblioshiny tools, determining the networks of connections between bibliometric entities: keywords, journals, authors, countries, and funding agencies. The analysis results were used to illustrate the dynamics of research topics over a 60-year publication history on the TIG process. Current research trends include, among others, the advancement of TIG welding variants to improve process efficiency, the application of artificial intelligence, the application of optimization methods, and deep learning. The most urgent research needs involve determining the weldability of special metals, assessing the environmental degradation of TIG-welded joints, and applying data mining techniques for the optimization of the TIG process. The study may serve as an objective, comprehensive, and author-unbiased complement to traditional systematic review articles on TIG welding and related processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.199 | 0.225 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".