Machining of titanium alloys by using micro abrasive jet machine: an experimental investigation
Bibliographic record
Abstract
Ceramics, silicon, glass, titanium and nickel alloys, and other difficult-to-cut materials are now widely used in the MEMS, electronic device, and aerospace industries. The increased cost is due to the machining of these materials. One of these materials’ most convenient micromachining technologies is micro abrasive jet machining (MAJM). This method has several distinct advantages, including a small heat-affected zone, low cutting forces, high machining versatility, and high flexibility. Fine abrasive particles (aluminum oxide or silicon carbide) and highly compressed air or gas (helium, nitrogen, or air) are directed on the target surface via a fine nozzle in this machining process. The abrasives exiting the nozzle at high speeds impinge on the target surface, causing material removal due to erosive action. This method had a very high etching rate compared to other micro-fabrication techniques. Furthermore, it does not require a clean room environment, making it particularly appealing for low-cost industrial practices for machining difficult-to-cut materials. This research aimed to create MAJM for difficult-to-machine materials like the titanium alloy (Ti-6Al-4V) plate. The new design and fabrication of the Laval nozzle were first reported in order to increase the machining productivity of micromachining. The circular cross-sectional nozzle was designed for high-speed, precise etching and patterning on difficult-to-machine materials. Using Taguchi’s design of experiment methodology, this study investigates the effect of various parameters such as air pressure, abrasive size, and standoff distance on machining performance. The analysis of variance (ANOVA) method was used to determine the significance of each factor. The developed MAJM experimental setup investigates whether the Laval nozzle reduces the dimensional variation of the machined hole.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".