Bibliographic record
Abstract
Abrasive jet micro-machining is a process that utilizes small abrasive particles entrained in a gas stream to erode material, creating micro-features such as channels and holes. Erosion experiments were carried out on aluminum 6061-T6, Ti-6A1-4V alloy, and 316L stainless steel using 50 μm A1₂O₃ abrasive powder launched at an average speed of 106 m/s. The dependence of erosion rate on impact angle was measured and fitted to a semi-empirical model. The erosion data was used in an analytical model to predict the surface evolution of unmasked channels machined with the abrasive jet at normal and oblique incidence, and masked channels at normal incidence. The predictions of the model were in good agreement with the measured profiles for unmasked channels at normal and oblique impact, and masked channels in at normal incidence up to an aspect ratio (channel depth/width) of 1.25. For the first time, it has been demonstrated that the surface evolution of features machined in metals can be predicted.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".