Controlled Depth Micro-Milling of Aluminum Oxide Using Abrasive Waterjet Machining to Create Micro-Molds with Free-Standing structures
Bibliographic record
Abstract
Abrasive waterjet machining has gained popularity due to the many advantages it possesses over conventional machining systems. These include the high achievable tolerance as well as minimal tool wear since it is a non-contact machining method. Previous studies utilized stainless steel masks cut using abrasive waterjet micro-machining (AWJM) together with abrasive slurry jet micro-machining (ASJM) to mill micro-molds in Al6061-T6 for the casting of polymeric microfluidic chips with intersecting micro-channels. However, the deflection of the slurry jet from the mask edge was found to lead to two undesirable effects: a trench along the edges of the raised structures representing the channels, and an undercut of the mask. Both effects were linked to the propensity of ductile materials to erode more rapidly at oblique incidence rather than perpendicular incidence. This thesis investigates the hypothesis that these effects can be greatly reduced by using AWJM to machine the molds into more brittle materials which erode in the opposite fashion, i.e., more rapidly at perpendicular than oblique incidence. To demonstrate this, AWJM was used to machine masks from aluminum oxide and subsequently mill micro-molds with raised intersecting free-standing structures into aluminum oxide substrates. By controlling the process parameters, it was found that for molds of identical depth, the undercut was reduced by five times and the undesirable erosion was reduced by about four-fold when compared to molds machined in Al6061 using the AWJM/ASJM hybrid technique. It was also found that much deeper molds could be made in aluminum oxide, with intersecting raised free-standing structures of up to 435 μm in height while still maintaining an acceptable surface quality, undercut, and undesirable erosion. Finally, since the technique involved only AWJM, it was much more convenient than the previously utilized hybrid AWJM/ASJM.
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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 | 0.000 | 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) | 0.001 | 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".