A Newtonian approach to predict the parameters required to machine high aspect ratio channels of prescribed topography
Bibliographic record
Abstract
Control of the microchannels’ cross-sectional shape may be of interest in micro-heat sinks, microfluidic particle sorting, and micro-machine lubrication applications. Previously, inverse methods have been used to determine the abrasive jet micromachining (AJM) traverse speed and path required to sculpt the desired cross-section for low Aspect Ratio (AR, the ratio of depth to width, see page xiv) topographies (<0.06). This thesisintroduces an iterative inverse method which allows prediction of the machining procedure required to sculpt high AR (>0.06-1) microchannels of prescribed cross-sectional shape using mask-less AJM. The predictions were experimentally verified for trapezoidal and semi-circular micro-channels and protruded features in borosilicate glass, and symmetric and non-symmetric wedges in poly-methyl-methacrylate (PMMA). Overall, the average accuracy of the machined profiles was 93.6 % in borosilicate glass and 91 % in PMMA. The methodology opens up new possibilities for the micro-fabrication of high-aspect-ratio micro-features of virtually any desired shape.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".