A Physics-Based Computational Model for the Cold Spray Deposition of Composite Coatings
Bibliographic record
Abstract
Abstract Composite coatings with tailored properties can be effectively deposited with the cold spray process via careful control of deposition parameters. To avoid repetitive experiments, numerical models are commonly used to optimize the cold spray deposition process parameters. The present study proposes using a physics-based hybrid computational approach to model the cold spray deposition of Ni-Ti/Al2O3 composite coating used for wear applications. The method involves using point cloud (for the impacting particles) and finite elements (for the deformed splats structures and substrate) to simulate dissimilar particles impact and interactions, plastic deformation, and temperature rise. The approach is computationally efficient and adequately captures the thermo-mechanical deformation resulting from the interactions among dissimilar particles. The simulations are carried out for various combinations of material types, particles sizes and shapes, and impacting velocities. The results from the simulations are analyzed and validated by comparing them with that of previous works. The plastic deformation and temperature rise within the mating bodies increase with increasing particles’ kinetic energies. The Ni-Ti-Al2O3 powder particles lead to higher plastic deformation, temperature rise, and inter-particle bonding due to the presence of the hard Al2O3 particles. The temperature does not rise above melting; however, recrystallization of coating microstructure becomes possible even at a low deposition rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".