Sculpting Desired Topographies Using Abrasive Jet Micro-Machining
Bibliographic record
Abstract
Abrasive jet micromachining (AJM) uses a jet of high-speed particles to erode a wide variety of materials. Given a set of process parameters, surface evolution models capable of predicting the shape of straight, constant-depth channels in a wide variety of materials are well-established. This dissertation presents novel methods for solving the unaddressed more challenging and industrially relevant inverse problem of determining the process parameters required to machine a particular user-specified feature topography. Since the air driven jet used in AJM is divergent, the edges of the desired features are usually defined using a mask which is attached to the surface of the target material. This dissertation presents alternate techniques using stationary or moving shadow masks that can be moved over the surface and maskless techniques, in order to allow direct writing of desired features on the surface. A mathematical framework is then introduced to determine the direct writing source velocity function and path required to create a desired shallow topography. It is also shown how the methodology can be used with existing surface evolution models to predict the feature shape at any depth. The methodologies are demonstrated to work well for the AJM of constant depth micro-channels with user-specified cross-sectional shape, gradient etched micro-channels with specified texture along their length, and pockets with texture in two perpendicular directions. Finally, a new technique is introduced that utilizes a rotating patterned mask in order to control the AJM erosive footprint size and shape. Models for predicting the rotating mask pattern required to create virtually any desired footprint are presented, and experimentally verified for symmetric and asymmetric W-shaped, trapezoidal and wedge shaped footprints.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".