Bibliometric analysis of AZ31 alloy welding: trends in the use of the friction stir welding process
Bibliographic record
Abstract
This study presents a bibliometric analysis of research related to lightweight alloys, particularly the AZ31 magnesium alloy, and the friction stir welding (FSW) process. A structured search using the keywords “Weld* AND AZ31*” was conducted in the Web of Science database. From the 1,681 retrieved articles, titles, keywords, and abstracts were analyzed to identify prevailing themes, commonly used terms, and emerging research trends. These trends were examined in the context of macroeconomic and socio-environmental factors, especially the Environmental, Social, and Governance (ESG) agenda and the role of lightweight alloys in reducing greenhouse gas (GHG) emissions. Magnesium alloys such as AZ31 are employed to produce components with up to 30% less mass than equivalent aluminum parts, contributing to lower fuel consumption and emissions in transportation. The choice of materials must consider the interaction between material, function, process, and form. In this regard, FSW, a solid-state joining process that eliminates the need for filler materials and shielding gases, is particularly effective for joining AZ31 alloys, reducing manufacturing costs and welding defects commonly associated with fusion-based methods. The analysis indicates a growing academic interest in AZ31 alloys, driven by their low density and favorable mechanical properties. However, a notable gap remains regarding multi-pass FSW and the use of machine learning techniques to predict weld behavior. Terms like “double side” or “double pass” appear in only 0.47% of the dataset. In comparison, co-occurrences of “FSW” and “Machine Learning” are limited to 0.3%, featuring techniques such as deep neural networks, decision trees, XGBoost, and random forests. The most active research groups are based in countries with high production and consumption of magnesium alloys, including China, India, Japan, the United States, and Canada. Brazil, despite being a major magnesite producer, imports metallic magnesium, highlighting the need for national policies to support technological development. This study contributes by identifying research gaps and proposing future directions in sustainable welding and manufacturing.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.154 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".