Bibliographic record
Abstract
Mechanical Engineering curriculum has been changing to increase the amount of design taught to students. Ideally students would manufacture and test their designs, as this process validates the quality of the design and gives invaluable feedback. Designs may not be constructed, however, where there are limitations on time students have for the building phase, where limited shop facilities are available, or where students don’t have the manufacturing skills necessary. Rapid prototyping machines can mitigate these issues, but their initial, support and consumable costs, along with their low productivity, make them inaccessible for most student projects. Even traditional shop construction of designs is of limited feedback value, since a non-functioning design could be the result of faulty design or of poor quality manufacture. This paper will explore the use of a laser engraver machine as a vehicle for low-cost 2D and 2-1/2D rapid prototyping of mechanical designs. Laser engraver machines have low initial (c.$10–20K) and operating costs. They are capable of cutting 2D parts from materials such as paper matte and illustration boards at cutting rates of one meter per minute or more, allowing high throughput of parts cut. Machines typically attach to computers through a printer driver, so operation is as simple as printing a drawing from CAD software. While individual parts are constrained to planar geometry, simple assembly materials (such as glue and small machine screws) allow designs with moving parts to be constructed and tested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".