Multi-criteria optimization of micro-hole on glass using developed <i>µ</i> -abrasive jet machine set-up
Bibliographic record
Abstract
In non-traditional machining, micro-abrasive jet machining (MAJM) is a cost-effective machining process. MAJM has been used for fabricating electronic devices and microfluidic channels. This work has made an effort to utilize MAJM for glass. A new design and fabrication of the Laval type of nozzle have been proposed to improve machining accuracy. A nozzle is conceived to ensure specific characteristics of the mixture (compressed air and abrasive particles) pass through it. The abrasive particle force is converted to kinetic energy, increasing the mixture’s velocity. The cross-sectional area of the nozzle can be circular, rectangular, square, or oval. A circular cross-sectional nozzle has been developed for high velocity, precise etching, and patterning on difficult-to-machine materials such as steel alloys. A circular cross-sectional micro-nozzle with a large aspect ratio is proposed, and the flow characteristics and cutting performance are examined precisely by the experiment. Efforts are being made to make machining processes sustainable, productive, and efficient. Here, the Taguchi-grey relational analysis integration approach has been used to analyze the machining parameters such as air pressure, stand-off distance, and abrasive mesh size (AMS). The top hole diameter, bottom hole diameter, material removal rate, and radial overcut are the response variables in this investigation. Analysis of variance (ANOVA) results showed that the AMS was the most efficient parameter, which followed the processing condition on the total input of the multi-purpose function. The reported optimized process parameters are air pressure of 8 bar, stand-off distance of 2 mm, and AMS mix (50%+100%) micron, which significantly affects the top and bottom micro-hole diameters.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 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".