Numerical Study of Copper Particle Deposition Process and Residual Stress During Cold Spraying
Bibliographic record
Abstract
In order to investigate the acceleration and deformation behavior of particles during cold spraying, the finite element method was used to simulate the acceleration process of copper particles with different sizes inside the Laval spray gun, and the accuracy of the simulation results was verified through particle image velocimetry. Meanwhile, the multi-particle collision model was established using a coupled Eulerian-Lagrangian method with Python script to simulate the deposition process of copper particles and analyze the residual stresses of the copper coating. The simulation results of particle acceleration indicated that the velocity of the same material increased as the particles size decreased under the same spraying conditions. The simulated particle velocity distribution closely matched the actual velocity distribution during spraying, with only a 3.5% difference in average values. Under the conditions of 3 MPa and 723 K, the collision process of particles at different moments was simulated, and the deposited particles were compacted by subsequent particles, causing severe deformation and filling of the pores between the deposited particles, forming a dense coating. The residual stress of the coating simulated by the multi particle collision model(-57.02 MPa) was close to the measured value(-42.68 MPa), demonstrating that this model could effectively reflect the formation of the coating and the distribution of internal residual stresses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".