Simulation and Experimental Validation of Splat Profiles for Cold-Sprayed CP-Ti with Varied Powder Morphology
Bibliographic record
Abstract
The cold spray (CS) process has gained momentum as an additive manufacturing technology, due to its low processing temperatures. Computational modelling can accompany CS experiments to optimise deposition parameters, as well as predict coating properties and their final performance. A commonly used plasticity model is the Johnson–Cook (JC) model; however, its accuracy is limited at the high strain rates typical of cold spray. This study aims to assess the robustness of predictions using a modified JC model, particularly for two material systems of commercially pure titanium (CP-Ti) and Al6061-T6, and feedstock powders of two sizes and three morphologies. CP-Ti powders of spherical and irregular morphologies were sprayed onto CP-Ti substrates using a Titomic TKF1000 cold spray system. The cross-sectional splat profiles and flattening ratios were compared against smoothed particle hydrodynamics (SPH) simulations. The deposition process of particles was simulated using a modified JC model, implemented as an ABAQUS (2020) VUHARD user subroutine programme. The results showed that SPH simulations predicted the depth of impact, the splat profiles and the flattening ratios. Additionally, the simulations indicated that the impacting particle temperature remained below the melting point of CP-Ti throughout the process. Lastly, it was demonstrated that the irregular CP-Ti feedstock showed greater tendency of restitution than spherical feedstock.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".