Evidence map›Paper›PMID 36616583›Full record

ArticlePolymers2023

Elasto-Plastic Mechanical Modeling of Fused Deposition 3D Printing Materials.

Francesco Bandinelli, Lorenzo Peroni, Alberto Morena

Open access · goldAbstract read
In one paragraph

Article in Polymers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
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

8 citing papers in PubMed, 40 citations in OpenAlex.

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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 at 1 institution in 1 country.

Francesco BandinelliDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy.
Lorenzo PeroniDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy.
Alberto MorenaDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy.ORCID 0000-0002-9417-0208
Politecnico di Torino · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fused deposition modeling (FDM) is one of the most common 3D printing technologies and is becoming a well-established production method. Short fiber-reinforced polymers represent a new class of printing materials that enhance the mechanical properties of final components, thus informing an interesting subject of analysis for this study. FDM-printed parts are characterized by a strong anisotropy, so their behavior should be analyzed accordingly. The authors proposed a modeling strategy based on a transversely isotropic behavior hypothesis, representing material behaviors associated with an elasticity matrix in relation to the elastic field and a combination of Hill's yield criterion and Voce's isotropic hardening law for the plastic field. Material properties of materials were experimentally identified through tensile tests on dog bone specimens printed with different orientations in space. The numerical model was then calibrated using the finite element software LS-DYNA and the optimization software LS-OPT. An agreement between numerical and experimental results showed the robustness of the modeling strategy proposed to describe the stress behaviors of printed materials until a maximum load is reached, while strain behaviors have yet to be correctly defined due to the difficulties associated with evaluating an equivalent deformation.

Indexed as

additive manufacturinganisotropic elasto-plastic behaviorcarbon–nylonfused deposition modelingHill criterionLS-DYNAmaterial model identificationtensile testsVoce hardening law

Identifiers

PMID36616583
PMCPMC9823949
OpenAlexW4313389246

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.