Evidence map›Paper›PMID 36998802›Full record

Article3D printing and additive manufacturing2023

Rotation-Assisted Separation Model of Constrained-Surface Stereolithography.

Min Hu, Haobo Cheng, Yunpeng Feng

Abstract read
In one paragraph

Article in 3D printing and additive manufacturing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Min HuJoint Research Center for Optomechatronics Design and Engineering, School of Optics and Photonic, Beijing Institute of Technology, Beijing, China.
Haobo ChengJoint Research Center for Optomechatronics Design and Engineering, School of Optics and Photonic, Beijing Institute of Technology, Beijing, China.ORCID https://orcid.org/0000-0003-0430-3941
Yunpeng FengJoint Research Center for Optomechatronics Design and Engineering, School of Optics and Photonic, Beijing Institute of Technology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Among a variety of additive manufacturing technologies, constrained-surface image-projection-based stereolithography (SLA) technology has unique advantages in printing precision and commercial maturity. For the constrained-surface SLA process, separating the cured layer from the constrained surface is a crucial step that enables the fabrication of the current layer to accomplish. The separation process limits the accuracy of vertical printing and affects the reliability of fabricating. To reduce the separation force, current existing methods include coating nonsticky film, tilting the tank, sliding the tank, and vibrating the constrained glass. Compared with the above methods, the rotation-assisted separation method presented in this article has the advantages of simple structure and inexpensive equipment. The results of the simulation show that the pulling separation with rotating can reduce the separation force and shorten the separation time effectively. Besides, the timing of rotating is also crucial. A customized rotatable resin tank is used in the commercial liquid crystal display-based three-dimensional printer to reduce the separation force by breaking the vacuum environment between the cured layer and the fluorinated ethylene propylene film in advance. The analyzed results have demonstrated that this method can reduce the maximum separation force and the ultimate separation distance, and the reduction is related to the edge profile of the pattern.

Indexed as

constrained-surface stereolithographyrotatingseparation forcevacuum environment

Identifiers

PMID36998802
PMCPMC10049864

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.