Modeling abrasive slurry jet machined micro-channel topography on curved surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modeling the abrasive slurry jet micro-machining (ASJM) process can provide a better understanding of the physics of the process so that the machined geometry can be better controlled. Previous models have been developed to analyze the evolution of the machined geometry for surfaces that are initially flat. The surface evolution of initially convex surfaces machined using ASJM is more complex, and has never been attempted, despite possible applications for microchannels on rods in biomedical and other applications. This paper uses computational fluid dynamics (CFD) to model the evolving topography of straight, axial, micro-channels on 304 SS rods subjected to multi-pass ASJM at 3 different standoff distances (SOD) using a garnet particle aqueous slurry. It was found that the point-particle assumption of most CFD codes introduced an error in the location and impact angles of particle strikes that strongly affected the predicted topography. Correcting for this error, and calibrating the model based on the depth of the first pass profile allowed the channel profiles for up to 8 nozzle passes to be predicted to within 5.3% of those measured at all SODs. The differences between machining rods and flat plates at various SODs were discussed and explained using the CFD analysis and particle tracking. It was shown how the initial surface curvature and the upstream particle and fluid velocity distributions at different SODs affected the resulting stagnation zones. These factors affected the locations of both the initial particle strikes, and the secondary impacts due to the secondary slurry flow within the eroded feature. The secondary strikes were sensitive to ratio of the jet footprint to the surface curvature. The low velocity particles were found to be responsible for widening the channel while those with high velocity deepen the channel. In summary, this paper shows for the first time that a numerical framework can be used to predict the surface evolution of straight channels machined using ASJM on curved surfaces. It successfully predicted the channel depth, the width, and the geometry and provided a comprehensive understanding of the flow and process mechanics. • Novel CFD-based model accurately predicts channel surface evolution on curved surfaces. • Secondary impacts affect channel shape evolution on curved surfaces more than flat. • Occurrence of secondary impacts strongly depends on ratio of curvature to SOD. • Novel correction for finite particle geometry to predict channel shape on convex surfaces. • Low-energy particles widen channels; high-energy particles deepen channels.
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.
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 it