Modeling and self-supporting printing simulation of fuse filament fabrication.

Xiaohui Ao, Shengxiang Lin, Jianhua Liu, Huanxiong Xia, Junfeng Meng

Abstract read

In one paragraph

Article in Scientific reports, 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.

Investigation on Bridging Defects in 3D-Printed Polylactic Acid Beams Using Fused Filament Fabrication.Polymers · 2026
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

5 authors.

Xiaohui AoSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Shengxiang LinSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Jianhua LiuSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Huanxiong XiaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China. hxia@bit.edu.cn.

Junfeng MengSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China.

Funding

National Key Research and Development Program of China No.2022YFB3403801National Natural Science Foundation of China 51905038National Natural Science Foundation of China 52105504SAST Foundation of CASC SAST2022-098

6 · The paper itself

Abstract

This study presented a comprehensive computational fluid dynamics-based model for fused filament fabrication (FFF) three-dimensional (3D) printing multiphase and multiphysics coupling. A model based on the framework of computational fluid dynamics was built, utilizing the front-tracking method for high precision of multiphase material interfaces, a fully resolved simulation at the mesoscale explores the underlying physical mechanism of the self-supported horizontal printing. The study investigated the influence of printing temperature and velocity on the FFF process, exhibiting a certain self-supporting forming ability over a specific range. The results indicated that during the printing of large-span horizontal extension structures, the bridge deck material transitions from initial straight extension to sagging deformation, ultimately adopting a curved shape. The straight extension distance is inversely proportional to the depth of the sagging deformation. Additionally, the study revealed that printing temperature primarily affected the curing time of the molten material, while printing velocity fundamentally affected the relaxation time of both thermal and dynamic characteristics of the material.

Indexed as

Additive manufacturingFused filament fabricationHeat transferMultiphase flowProcess modeling

Identifiers

PMID39009739

DOI10.1038/s41598-024-67200-9

PMCPMC11251272

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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.