MétaCan
Menu
Back to cohort
Record W4238259055 · doi:10.32920/ryerson.14664447

Inverse Identification Of Elastic Moduli For 3D Printed PLA, Using Impulse Excitation Technique (IET)

2021· preprint· en· W4238259055 on OpenAlexaff
Afridi Mohsin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceYoung's modulusUltimate tensile strengthAnisotropyComposite materialModalVibrationImpulse (physics)ModulusTensile testingFinite element methodElastic modulusStructural engineeringAcousticsEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

3D Printing has recently undergone extensive development due to its lower cost and flexibility. A number of studies have been carried out to determine 3D printed material properties. This study focuses on the determination of the dynamic properties for PLA. The PLA material is processed through the popular FDM method with three different build orientations. A vibration experiment is conducted to evaluate the first modal frequency and Young’s modulus. The results are then compared to the FEM modal analysis and finally the traditional tensile testing results. The anisotropy of the 3D printed components, mainly due to the density changes caused by voids and filament alignment, result in the variation of the Young’s modulus which is different than the homogenous bulk material. The calculated Young’s moduli values are very slightly higher than the tensile test results which is in conformance with the trend documented by earlier studies on similar printed materials using the same techniques

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.409
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 science0.0000.000
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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207