Erosion and Roughness Modeling in Abrasive Jet Micro-machining of Brittle Materials
Bibliographic record
Abstract
The effect of particle size, velocity, and angle of attack was investigated on the roughness and erosion rate of unmasked channels machined in borosilicate glass using abrasive jet micro-machining (AJM). Single impact experiments were conducted to quantify the damage due to the individual alumina particles. Based on these observations, an analytical model from the literature was modified and used to predict the roughness and erosion rate. \nA numerical model was then developed to simulate the brittle erosion process leading to the creation of unmasked channels as a function of particle size, velocity, dose, impact angle and target material properties. For the first time, erosion was simulated using models of two damage mechanisms: crater removal due to the formation and growth of lateral cracks, and edge chipping. Accuracy was further enhanced by simulating the actual relationship between particle size, velocity and radial location within the jet using distributions measured with high-speed laser shadowgraphy. \nThe process of post-blasting AJM channels with abrasive particles at a relatively low kinetic energy was also investigated in the present work by measuring the roughness reduction of a reference unmasked channel in borosilicate glass as a function of post-blasting particle size, velocity, dose, and impact angle. The numerical model was modified and used to simulate the post-blasting process leading to the creation of smooth channels as a function of particle size, velocity, dose, impact angle, and target material properties. \nFinally, the effect of alumina particle kinetic energy and jet impact angle on the roughness and erosion rate of channels machined in borosilicate glass using abrasive slurry jet micro-machining (ASJM) was investigated. The analytical and numerical models derived for AJM, were found to predict reasonably well the roughness and the erosion rate of ASJM channels, despite the large differences in the fluid media, flow patterns, and particle trajectories in AJM and ASJM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".