A study on material-process interaction and optimization for VAT-photopolymerization processes
Bibliographic record
Abstract
Purpose This paper aims to present an investigation of material-process interaction of VAT-photopolymerization processes. The aim of the research is to evaluate the effect of different printing factors on the tensile properties, such as elastic modulus, of 3D printed specimens. Design/methodology/approach To perform this study, Design of Experiments is used by the use of Taguchi’s techniques. The relationship between each factor and the elastic modulus, ultimate tensile stress and strain at break is obtained. Furthermore, the total print time is analyzed with respect to the obtained properties. Findings The study indicates that part orientation, exposure time to the UV light and layer thickness are the most important factors affecting the investigated properties. At the same time, it was found that the highest mechanical properties can be obtained with the shortest printing times. A comprehensive list of factors available on the slicing software and other factors, like the orientation of the part or its position, is investigated. Future studies including post curing and chemical characteristics based on the obtained results are necessary. Originality/value As a result of this research, it is outlined that using design for additive manufacturing for vat-photopolymerization, especially on DLP processes, 3D printing methods can be stablished. Furthermore, it outlines the possibility of tailoring mechanical properties of printed parts as a function of print parameters and print time. Considering the limited amount of information available in the open literature, the results presented in this paper are of great interest for researchers in the field of VAT-photopolymerization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".