Bibliographic record
Abstract
Low resolution printing results in fused joints when the joint clearance is intended to be very small. Various 3D printers are capable of print resolutions of up to 600dpi (dots per inch) as quoted in their datasheets. It is imperative to include the ability of a 3D slicing application, to validate 3D models, based on the ability of the printer to properly produce the features with the smallest detail in a model. A way to perform this validation would be the physical measurement of printed parts and comparison to expected results. Our method uses ray casting to detect features in the 3D models whose sizes are below the minimum allowed by the printer resolution. Our model was tested using few simple and complex 3D models. Areas in the slices with thickness less than the specified resolution were detected. Our model serves two purposes: (a) to assist CAD model designers in developing models whose printability is assured- by warning or preventing shape operations that will lead to regions/features with sizes lower than that of the printer resolution; (b) to validate slicing outputs to identify regions/features with sizes lower than the printer resolution. This makes our model very powerful in the quality assurance of 3D printing and a huge cost/time saver when planning for 3D printing.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".