Laser shadowgraphy measurement of abrasive particle mass, size and velocity distributions through micro-masks used in abrasive jet micro-machining
Bibliographic record
Abstract
In abrasive jet micromachining (AJM), a jet of particles is passed through narrow mask openings in order to define the features to be micro-machined. The size and shape of the micro-machined features depends on the distribution of the particle velocity and mass flux through the mask openings. In this work, a high speed laser shadowgraphy technique was used to demonstrate experimentally, for the first time, the significant effect of the mask opening size and powder shape and size on the resulting distribution of particle mass flux and velocity through the mask opening. In particular, it was found that the velocity through the mask was approximately constant, but different in magnitude than the velocity in the free jet incident to the mask. The measured mass flux distributions were in excellent agreement with a previously developed analytical model, thus directly confirming its validity. Additional measurements also showed that an existing numerical model could be used to predict the velocity distribution in free jets of spherical particles, and, if a modification to the particle drag coefficient is made, in free jets of angular particles. The direct experimental verification of these models allow for their use in surface evolution models that can predict the evolving shape of features micro-machined using AJM.
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 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".