Lasers and Thermal Spray
Bibliographic record
Abstract
Basically, thermal spray and laser processing can be considered as half brothers since they show many common features due to the use of a (more or less) high-energy source for both. Their combination can therefore be very fruitful and prominent to achieve coatings, which results in their most recent and advanced applications. In the materials processing development story, the laser will thus have moved from cutting to coating. This keynote presentation focuses on the recently-developed coupling of laser processing to cold spray). In this dual process, a cold spray gun is combined to a laser head in a single device, e.g. on a robot. Series of coating experiments using various laser irradiation conditions, primarily pulse frequency, were carried out for Al-based and Ni-based alloys. Laser pre-treatment of the substrate just prior to cold spray, was shown to be beneficial for adhesion of cold-sprayed coatings. Adhesion improvement was exhibited and studied from LASATesting (LASAT for “LAser Shock Adhesion Test”). Incidentally, through LASAT also, the role of lasers in the development of thermally-sprayed coatings can be considered as major. Results are discussed in the light of a TEM (Transmission Electron Microscope) study of the coating-substrate interface with and without laser pre-treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".