The PROTAL Process Applied on Cold Spraying to Improve Interface Adherence and Coating Cohesion—Case of Titanium and Nickel Based Alloys
Bibliographic record
Abstract
Abstract Cold spraying is particularly suitable for elaborating heat and oxidation sensitive coatings. Due to the fact that the particles are not melted during the spraying process, it is thus possible to elaborate coatings without chemical modifications. Nevertheless, according to the materials considered, some interface defects can be detected inducing an inadequate adhesion between the substrate and the coating. Bonding mechanisms are not only strongly dependent on the particle velocity but also on the state of the surfaces. By this way, surface pre-treatments can be necessary to improve adhesion. From all the surface modification technologies, laser ablation process is very interesting due to its flexibility by using optical fibers and due to the perfect control over the treated area. It is then possible to interact with the material during all the spraying process on the substrate surface as well as on the interface layers. This is particularly the aim of this study which consists in exploring the laser influence, implementing the PROTAL process, on the different interfaces quality for coatings elaborated by cold spray on metallic substrates. By controlling the chemical composition of the materials, the coating cohesion as well as the adhesion level, coatings were sprayed on pure titanium and titanium and nickel based alloy substrates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".