Fabrication of Free-Standing Structures Using Abrasive/Slurry Jet Micro-Machining
Bibliographic record
Abstract
Many advanced engineering materials are machined using abrasive water jets, a non-traditional machining method that has certain advantages compared to other approaches. This dissertation aims at developing methodologies to use abrasive water and slurry jets to micro-machine free-standing structures for heat sink and microfluidics applications. The first aim was to fabricate high aspect ratio free-standing structures with a minimum distance between each other that would lead to maximum heat transfer performance in heat sinks. The effect of machining process parameters on the quality of the resulting free-standing structures was studied. It was demonstrated that high-quality free-standing structures with aspect ratios of more than 40 to be used as fins in heat sinks could be fabricated. There is an increasing demand for metallic micro-molds that can be used for inexpensive mass production of polymeric microfluidic chips. Therefore, the feasibility of using abrasive water jet micro-machining (AWJM) and abrasive slurry jet micro-machining (ASJM) to fabricate such micro-molds was studied. Jet raster scans under various combinations of process parameters were used in order to machine micro-pockets containing free-standing structures, representing molds for casting microfluidic chips with channel networks. The optimum process parameters and material were determined in order to fabricate the molds with the best possible surface finish. Because unmasked machining could not be used to fabricate molds with sharp-edged intersecting features, a hybrid AWJM/ASJM masked machining technique was introduced. By careful selection of the process parameters, high quality molds with both single and intersecting free-standing structures at multiple heights could be fabricated, thus making three-dimensional microfluidic chip mold fabrication feasible. Finally, a new method of mold fabrication that utilized water jet cutting technology to cut free-standing structures out of mild steel sheets, to be used as a mold for PDMS casting, was developed. This enabled 3D features having very abrupt changes in depth with almost 90o angles for use in microfluidic devices. As a proof-of-concept, a polydimethylsiloxane (PDMS) chip was made and tested. It was shown that this alternative approach to create microfluidic molds is versatile and may find utility in reducing the cost and complexity involved in fabricating 3D features in microfluidic devices.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".