Track cross-sectional profile model for time-invariant deposition processes — Applied to cold spray and aerosol jet printing
Bibliographic record
Abstract
Modeling the track cross-sectional profile (CSP) in deposition processes is critical for assessing and controlling deposition quality. This article focuses on modeling time-invariant deposition (TID) processes, where deposited material does not move after impact with the evolving surface, and deposition efficiency remains constant. For a TID process, the CSP can be computed from the mass flux distribution using the Abel integral transform. The model is validated for cold spray (CS) and aerosol jet printing. Using the TID assumption enables the modeling of CS and AJP tracks from dot deposition data with height and width errors down to 11 and 22% for CS, and 7 and 21% for AJP. The error of this model when considering short and curved tracks is discussed, as well as the effects of nozzle standoff distance and tilt. Fast methods for arbitrary CSP computations and a fast CS method considering varying deposition efficiency are also discussed.
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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".