Modeling eroded topography in masked abrasive slurry jet pocket milling
Bibliographic record
Abstract
Abrasive slurry and water jets can be used together with erosion-resistant masks to rapidly machine micro-pockets. However, the use of masks can result in an undesirable erosion and mask under-etching which can locally increase the depth twofold or more in the vicinity of the mask edges. Although the detailed mechanisms leading to the undesirable erosion are not well understood, they appear to be related to the interaction of the jet flow with the mask edge. This paper employs novel experimental techniques and coupled computational fluid dynamics/surface evolution models to rigorously study these mechanisms for the first time. To demonstrate the techniques, the abrasive slurry jet micromachining of pockets into Al 6061-T6 using SS304 masks was considered, using a novel technique to precisely control the position of the abrasive slurry jet relative to the mask edge. The model reasonably accurately predicted the surface evolution and undesirable erosion in various scenarios, as well as the physics of mask under-etching. The position of the jet relative to the mask edge and the scanning direction were found to strongly affect the extent of undesirable erosion. The model suggests that the stagnation zone in masked milling is smaller than that in unmasked milling, and that this facilitates the formation of slurry recirculation zones near the mask edges which, together with particle ricochets off the mask edge, interact to create the undesirable erosion and under-etching. Based on this improved understanding, several path strategies were presented that were found to minimize the undesirable erosion and thus allow the milling of pockets with more uniform depths.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".