Computed Tomography-Based Volumetric Additive Manufacturing: Development of a Model Based on Resin Properties and Part Size Interrelationship—Part I
Bibliographic record
Abstract
This study presents an analytical description of the computed tomography-based volumetric additive manufacturing (VAM) process, with an emphasis on the impact of resin properties on product dimensions. The main issue addressed in this study is the assessment of the dimensional limitation of the objects produced using the VAM process, which is usually reported to be of the order of one centimeter. An analytical model is introduced to predict the product size based on the resin property (penetration depth—Dp), vial size (radius), and the duration of part formation, and the results indicate significant correlations among these parameters. A method of Dp measurement and analysis that is appropriate for the VAM process is also introduced. Mathematical justification is provided along with experimental verification for the effects of the main governing factor, Dp, on the maximum possible product size. Multiple criteria are also introduced for selecting the appropriate size of the resin container (vial) based on the desired object size and the value of Dp. It was found that the Dp is a crucial factor in analysis and experimentation in the VAM process, and its value is fundamentally different from the one obtained in the conventional polymerization AM methods. The product dimension based on the resin property, vial size, and time for the formation of the part is introduced by the analytical model. This model provides valuable insights into the complex interplay of factors influencing VAM outcomes and can facilitate informed decision-making in material selection and process design.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".