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Record W4382049141 · doi:10.32920/23582187.v1

An Investigation Into Fundamental Frequency Of Intact And Defective 3D-Printed PLA Beams

2023· preprint· en· W4382049141 on OpenAlexaff
Ali Foroozanfar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPolylactic acidCantileverMaterials science3D printingImpulse (physics)Beam (structure)Fundamental frequencyComposite materialFlexural strengthStructural engineering3d printedUltimate tensile strengthFinite element methodDisplacement (psychology)ModulusAcousticsEngineering

Abstract

fetched live from OpenAlex

<p>3D printing manufacturing method gains significant attractions from industries due to its advantages over other production methods. Polylactic Acid (PLA), a biodegradable feedstock in 3D printing and one of the major filaments in the market, is investigated. Impulse Excitation Technique (IET) is used to obtain Young’s modulus of 3D-printed PLA specimens. The results are then compared with those achieved through experimental tensile testing. Displacement laser sensors, attached on the vertical base along cantilever 3D printed PLA cantilever beam samples, are used to capture the fundamental flexural frequencies of the beams, and are compared with those obtained from ANSYS FEM models. The effect of through-the-thickness cracks on the fundamental frequency of the defective samples is also studied, both experimentally and numerically. Samples including deliberately printed cracks of different depths and locations were examined through experiment and simulation. Results showed that the crack distance from the fixed-end has a more pronounced effect on fundamental frequency than the crack size. Finally, variation of numerically calculated fundamental frequency of defective beam models vs. crack depth and location are presented, which could be used as a database and pave the road to more elaborate crack identification techniques. </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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.355
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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