Evidence map›Paper›PMID 39597110›Full record

ArticleMicromachines2024

A Scalable Digital Light Processing 3D Printing Method.

Junjie Huang, Jiangkun Cai, Chenhao Huangfu, Shikai Li, Guoqiang Chen, Hao Yun, Junfeng Xiao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2024. 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

7 authors.

Junjie HuangSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Jiangkun CaiSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Chenhao HuangfuSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Shikai LiSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Guoqiang ChenSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Hao YunSchool of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454003, China.
Junfeng XiaoShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518000, China.

Funding

Fundamental Research Funds for the Universities of Henan Province No.NSFRF210339Henan Province Scientific and Technological Research Plan No.242102221050Key Discipline of Mechanical Engineering in Henan Polytechnic University and the University Doctoral Fund No.B2016-24Key Research and Development & Transformation Project of Qinghai Province No.2024-QY-211
6 · The paper itself

Abstract

The 3D printing method based on digital light processing (DLP) technology can transform liquid resin materials into complex 3D models. However, due to the limitations of digital micromirror device (DMD) specifications, the normal DLP 3D printing method (NDPM) cannot simultaneously process large-size and small-feature parts. Therefore, a scalable DLP 3D printing method (SDPM) was proposed. Different printing resolutions for a part were designed by changing the distance between the projector and the molding liquid level. A scalable DLP printer was built to realize the printing resolution requirements at different sizes. A series of experiments were performed. Firstly, the orthogonal experimental method was used, and the minimum and maximum projection distances were obtained as 20.5 cm and 30.5 cm, respectively. Accordingly, the layer thickness, exposure time, and waiting leveling time were 0.08 mm, 3 s, and 6 s and 0.08 mm, 7 s, and 10 s. Secondly, single-layer column feature printing was finished, which was shown to have two minimum printing resolutions of 101 μm and 157 μm at a projection distance of 20.5 cm and 30.5 cm. Thirdly, a shape accuracy test was conducted by using the SDPM. Compared with the NDPM, the shape accuracy of the small-feature round, diamond, and square parts was improved by 49%, 42%, and 2%, respectively. This study verified that the SDPM can build models with features demonstrating high local shape accuracy.

Indexed as

3D printer3D printing resolutiondigital light processingscalable printingshape accuracy

Identifiers

PMID39597110
PMCPMC11596712

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.