Microstructure and Mechanical Properties of Ti Cold-Spray Splats Determined by Electron Channeling Contrast Imaging and Nanoindentation Mapping
Bibliographic record
Abstract
Cold spray is a thermo-mechanical process where the velocity of the sprayed particles affects the deformation, bonding, and mechanical properties of the deposited material, in the form of splats or coatings. At high strain rates, the impact stresses are converted into heat, a phenomenon known as adiabatic shear, which leads to grain re-crystallization. Grain re-crystallization and growth are shown to have a direct impact on the mechanical properties of the cold-sprayed material. The present study ties the microstructural features within the cold-sprayed Ti splats and the substrate to the bonding mechanism and mechanical properties. High-resolution electron channeling contrast imaging, electron backscatter diffraction mapping, and nanoindentation were used to correlate the microstructure to the mechanical properties distribution within the titanium cold-spray splats. The formation of nanograins was observed at the titanium splat/substrate interface and contributed to metallurgical bonding. An increase in grain re-crystallization within the splat and substrate materials was observed with pre-heating of the substrate. In the substrate material, the predominant mechanism of deformation was twinning. A good relationship was found between the hardness and distribution of the twins within the substrate and the size distribution of the re-crystallized grains within the splats.
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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".