Influence of the particle morphology on the spray characteristics in low-pressure cold gas process
Bibliographic record
Abstract
Low Pressure cold gas spraying (LPCGS) technology is gaining widespread use across various applications, including coatings, additive manufacturing, repair, and surface micro structuring. Process efficiency largely depends on particle collision velocity and spray angle, with particle morphology significantly influencing acceleration behavior within the Laval nozzle due to flow forces. Previous studies have analyzed these factors through simulations using simplified particle shape parameters, while experimental research often lacked representation of complex real morphologies. This study explores the impact of particle morphology on spray characteristics in LPCGS by examining three copper powders ( d p 1–40 μ m) with distinct shapes and micro structures. A detailed morphology analysis was performed using 2D light microscopy of projection area and 3D X-ray micro-computed tomography ( μ CT) imaging of real volumetric particle shape. The measured median sphericities vary from 0.76 to 0.96 and thus represent a broad shape factor spectrum. The results reveal that irregular particles experienced greater acceleration and produced a more focused spray pattern, whereas spherical particles attained lower maximum velocities and exhibited broader dispersion within the jet.The discrepancies in particle focusing, as measured, can reach up to 30% when comparing spherical and irregular particles. These insights underline the importance of particle morphology in optimizing cold spray processes for advanced applications.
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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 | 0.000 |
| 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".