Evidence map›Paper›PMID 39360137›Full record

Review3D printing and additive manufacturing2024

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process.

Miao Liu, Zhongqiu Liu, Baokuan Li, Yong Gan

Abstract readReview
In one paragraph

Review in 3D printing and additive manufacturing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Miao LiuKey Laboratory for Ecological Metallurgy of Multimetallic Mineral (Ministry of Education), Northeastern University, Shenyang, China.ORCID https://orcid.org/0000-0002-1356-9234
Zhongqiu LiuKey Laboratory for Ecological Metallurgy of Multimetallic Mineral (Ministry of Education), Northeastern University, Shenyang, China.
Baokuan LiSchool of Metallurgy, Northeastern University, Shenyang, China.
Yong GanChina Iron & Steel Research Institute Group, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metal-based additive manufacturing (MAM) method with high freedom and special fabricate technology has presented great universality in the aerospace and biomedicine field. However, a wide range of process parameters in the MAM method challenge the experimental study on the formation and evolution of defects. The numerical simulation presents its excellent accuracy and economy in predicting the evolution of multiphysics phenomena and was hence widely applied. In the current review, the available MAM methods with the fundamental phenomena were reviewed. Based on scales, numerical approaches divided into three categories were discussed and focused on their main prediction objectives and strengths or weaknesses of all the scales. To display the prediction results closer to real physical phenomena, advanced multiscale models coupled with various single-scale models are provided. The high prediction accuracy and computational efficiency enable better parameter control and defect avoidance. As a supplement and development to the physical-driven model, the data-driven model provides a new perspective on MAM methods. Based on the database generated by the physical-driven model and experiment, the data-driven models without calibration of input parameters are shown. In addition, this review discussed the development direction of numerical simulation, aiming to provide a reference for technical research in this field.

Indexed as

data-driven modelmetal-based additive manufacturingmultiphysics simulationmultiscale modelnumerical simulation

Identifiers

PMID39360137
PMCPMC11443132

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.