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Record W4402031551 · doi:10.32920/26862382

A Numerical Investigation Into the Natural Frequency Analysis of Delaminated 3D-Printed PLA Beams Including Contact

2024· preprint· en· W4402031551 on OpenAlexaff
Yousef Tabe

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNatural (archaeology)Natural frequencyMaterials scienceComposite materialAcousticsGeologyPhysicsVibration

Abstract

fetched live from OpenAlex

<p>This report includes the results of experimental and numerical modal analyses, performed to determine the flexural fundamental natural frequency of the 3D-printed PLA beam with delamination. Two types of 3D-printed samples, having 0.1mm and 0.5mm delamination thicknesses, are studied. The results of numerical modal analysis are compared and verified against those obtained experimentally. The Finite Element Analysis (FEA), carried out numerically using ABAQUS software, was performed for six cases. For a sample with 0.1mm delamination thickness, two analyses were performed by first excluding and then in the presence of contact/friction in the delamination zone. Another specimen, with a 0.5mm-thick delamination region, was also investigated, where two sets of studies were conducted. The delamination region was first modeled as uniform and was then refined to include the geometric non-uniformity resulting from the 3D-printing process. The latter model exhibits a more realistic representation of the real sample. In each case, two numerical modal analyses were performed, without and with friction contact in the delaminated segment. At the end, a comparison is made between all these models to show the effect of friction, delamination thickness, as well as uniform vs realistic modeling of the delamination, on the bending fundamental frequency of the PLA delaminated beam.</p>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.318
Teacher spread0.282 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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