Abrasive slurry jet machining of maskless helical micro-channels on rods: Process modeling using computational fluid dynamics
Bibliographic record
Abstract
A novel lathe setup and computational fluid dynamics approach were employed to assess the feasibility of using miniature abrasive slurry jets to direct-write helical micro-channels into 5 mm stainless steel rods. Multiple nozzle passes at offsets between 0 and 2.5 mm produced maskless channels of various depths and widths at a helix angle of 7.25°. Channels at aspect ratios up to ~0.5 were produced at depths of ~340 μm with a maximum depth variation of 2.4 %. At zero offset, the rods failed at a critical depth due to bending-induced stresses. Finite element analysis confirmed that using an offset reduced these stresses. At lower offsets where the jet footprint was fully on the rod, the channel depth increased linearly with the number of passes, while the channel width initially increased and then plateaued. An optimal offset of 1 mm yielded the narrowest and deepest channels. A CFD-aided model simulating the fluid flow, particle trajectories, and evolving channel topography was used to explain these trends. It utilized a virtual wall technique to correct particle impact data and predicted the eroded channel topography with a maximum error of 5.6 %. Variations in stagnation zone pressure and location at different offsets affected the particle trajectories and impact velocities. At the optimal offset, the particles at the jet centerline were less affected by the stagnation zone, thus maximizing their flux and impact velocity. This is the first study to demonstrate rapid direct-writing of helical micro-channels on rods using abrasive slurry jets, and it may have significant applications in inertial microfluidics.
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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 | 0.000 |
| 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 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".