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Record W2738984755 · doi:10.1115/msec2017-2949

Characterization of Material Behavior of the Fused Deposition Modeling Processed Parts

2017· article· en· W2738984755 on OpenAlexaff
Madhukar Somireddy, Aleksander Czekanski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsYork University
Fundersnot available
KeywordsOrthotropic materialFinite element methodFused deposition modelingMaterials scienceComposite materialCharacterization (materials science)Ultimate tensile strengthDeposition (geology)Layer (electronics)Elastic modulusTensile testingMaterial propertiesStructural engineering3D printingEngineeringNanotechnology

Abstract

fetched live from OpenAlex

In the present research, one of the additive manufacturing techniques, fused deposition modeling (FDM) fabricated parts are considered for investigation of their material behavior. The FDM process is a layer upon layer deposition of a material to build three dimensional parts and such parts behave as laminated composite structures. Each layer of the part acts as a unidirectional fiber reinforced lamina, which is treated as an orthotropic material. The mesostructure of a part fabricated via fused deposition modeling process is accounted for in the investigation of its mechanical behavior. The finite element (FE) procedure for characterization of a material constitutive law for the FDM processed parts is presented. In the analysis, the mesostructure of the part obtained via FDM process is replicated in the finite element models. Finite element models of tensile specimens are developed with mesostructure that would be obtained from FDM process, then uniaxial tensile test simulations are conducted. The elastic moduli of a lamina are calculated from the linear analysis and the strength parameters are obtained from the nonlinear finite element analysis. The present work provides a FE methodology to find elastic moduli and strength parameters of a FDM processed part by accounting its mesostructure in the analysis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.219
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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