Cold Spray for Additive Manufacturing: Possibilities and Challenges
Bibliographic record
Abstract
Cold spray (CS) is a deposition technique to form a coating from the particles with temperature lower than their melting point. In this technique, particles are accelerated by a supersonic flow of a carrier gas such as air or nitrogen. Upon impact, particles undergo significant plastic deformation that bonds them to the substrate. Since the particles are not molten, this deposition method does not apply a lot of heat to the substrate and this makes CS the best candidate for temperature sensitive and oxygen sensitive materials. CS can be adapted to form 3D objects following layer-by-layer approach. This is called cold gas dynamic manufacturing (CGDM) or cold spray as additive manufacturing. Developing complex shapes by CGDM may result in formation of inclined surfaces, corners and sharp edges. Deposition in those regions is often accompanied with challenges that affect the accuracy and efficiency of the manufacturing. In this study, CGDM for two typical shapes such as cylinder and frustum on a flat substrate has been simulated to represent the additively manufactured parts. Particle trajectories and impact conditions i.e. velocity and size distributions have been compared. The results of numerical modelling provided useful information for understanding the limitations and challenges associated with CGDM that can help us to improve the quality and precision of particle deposition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".