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Record W4214616731 · doi:10.1177/09673911221078481

A micromechanical approach to the mechanical characterization of 3D-printed composites

2022· article· en· W4214616731 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymers and Polymer Composites · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHomogenization (climate)Materials scienceComposite materialMicromechanicsVoid (composites)3d printedComposite numberFinite element methodFused deposition modelingMaterial properties3D printingStructural engineeringBiomedical engineering

Abstract

fetched live from OpenAlex

Aiming for the development of experimentally validated computational models to predict the mechanical properties of 3D-printed composites, the present study proposes a micromechanical approach by using a simplified unit cell model to characterize the material properties and behavior of 3D-printed composites manufactured through fused deposition modeling. The effective properties of the voided polymer matrix phase of the material are computed by calculating the void density as a tensorial meso-structural variable. These effective properties along with those of the fiber are input into a simplified micromechanical model to predict the material properties of the 3D-printed composite. The predictions are seen to be in very good agreement with the experimental values. The present approach is much simpler and less computationally costly compared to the finite element homogenization method. In addition, the present approach has the potential to simulate the response of the 3D-printed composite under different loading conditions.

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.

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 score0.014
Threshold uncertainty score0.594

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 head0.010
GPT teacher head0.195
Teacher spread0.185 · 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