Scientific and technological landscape of laser cladding: A bibliometric analysis of patents and publications
Bibliographic record
Abstract
In the last 20 years, public and private organizations involvement in laser cladding R&D activities is increasing. These activities are mainly conducted in universities, public research institutes and technical centres of private companies worldwide. This study presents a bibliometric analysis of the patents and scientific publications in the laser cladding field for the period ranging from 1985 to 2005 and seeks to identify the activity and trends in its environment for strategic purposes. All the laser cladding processes and all the substrates (steel, aluminium and superalloys) used for coating, repairing and 3D fabrication were taken into account. At first, the world patent production was analysed in terms of volume (580 patent families found since 1985), frequency and applications. Then the same strategy was applied to the scientific publications for a total volume of 588 targeted papers. Using bibliometric techniques, an analysis and mapping of the information was performed to highlight the temporal, geographical and institutional aspects of R&D activities. The patented applications were also classified in order to identify opportunities. This study shows the evolution of the scientific and technological environments of the laser cladding technology and can help public or private organizations to generate new ideas, gain awareness of emerging trends and validate the relevance of projects.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.081 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".