Tolerance Control Using Subvoxel Gray-Scale DLP 3D Printing
Bibliographic record
Abstract
3D printing manufacturing technology has been utilized in various applications due to its promising manufacturing advantages. Desktop Digital Light Processing (DLP) printers provide high-resolution products with a moderate price range. DLP uses an array of micromirrors to transmit UV light from the light projector in order to perform selective curing of a prepolymer resin and turn it in to the required geometry. The CAD file is transformed into several slices according to the layer thickness. Each slice is then converted to an image of black and white pixels, in which each white pixel actuates a corresponding micromirror to transmit the UV light to cure a corresponding voxel, while a black pixel corresponds to no actuation, which means no curing for the corresponding voxel. The micromirror’s size determines the resolution of the printer. Although a theoretical voxel size can be determined as a function of the micromirror’s dimensions and layer thickness, the actual voxel volume depends on several parameters such as the layer thickness, UV exposure time, and UV exposure intensity. Controlling these three parameters would result in more accurate 3D printed parts and more control over the dimensional tolerance. In this paper, the effect of variable light intensity in terms of grayscale pixels is studied along with the exposure time and layer thickness to manipulate the voxel horizontal dimensions. This enables printing with voxel dimensions below the size of the micromirrors in the DLP, which improve the geometric dimensioning and tolerance of the printed parts.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".