Numerical Study on Particle Behavior and Deposition Accuracy in Cold Spray Additive Manufacturing
Bibliographic record
Abstract
Cold Spray additive manufacturing (CSAM) is an emerging technique to fabricate freestanding objects by depositing solid-state layers of materials. Thanks to its remarkable deposition rate and maneuverability, it can be tailored to manufacturing intricate geometries in aerospace industries. In comparison to other additive manufacturing techniques, it is the processing speed, solid-state deposition, and the cost that make CSAM unique. In this study, CSAM process was modeled for a system comprised of a high-pressure cold spray gun with axial powder injection. To represent the flow structure around the already built objects and the deposited layers of CSAM, three walls with different profiles are placed on a flat substrate. In this work, the gas-particle behaviors are studied at the vicinity of these non-axisymmetric objects that can be generalized to more complex geometries and the applications of CSAM. The model is 3D and aluminum and copper powders were used for the feedstock. The particles’ conditions upon impact, such as particles’ footprint and normal impact velocities are studied. The numerical results show that the deviation of particles which is caused by the supersonic flow inside the nozzle and the shock waves outside the nozzle defines the accuracy of the deposition. Furthermore, the results manifest the particle’s material and size have a significant influence on the acquired velocities and trajectories of the particles, and consequently on the resolution of the process. It is found that the profile of the deposited layers has some effects on the gas flow near the substrate which plays a role in the dispersion of fine particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".