A study on material-process interaction and optimization for VAT-photopolymerization processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it