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Record W4399922635 · doi:10.18280/rcma.340312

Designing Cantilever Models from Various Materials and Comparing Them When They are under Constant Load and Have Holes

2024· article· en· W4399922635 on OpenAlexvenueno aff
Emad Toma Karash, Hussein Mohammed Ali, Mohammad Takey Elias Kassim

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsConstant (computer programming)CantileverMaterials scienceComposite materialComputer science

Abstract

fetched live from OpenAlex

The current research dealt with the system of cantilever beam, which has holes of various shapes on its surface, under the influence of direct external load.The design of the system control of four three-dimensional models consisting of steel and various composite materials was modeled and simulating by the use of ANSYS program, using finite element technology.The results of modeling in the ANSYS program using the finite element technique show that the values of the deflection in the models consisting of different composite materials increased by various percentages when compared to the model consisting of steel, with the model consisting of fiber class having the highest value.The displacements in these models also increase at rates almost similar to the percentage of deflections.According to the results, the various stresses that resulted from the steel model in the composite models were reduced by around a third, except for shear stress values, they increased in the composite material models, by more than a third compared to the shear stress in the steel model.The results of strains indicate an increase in the models composed of composite materials in different proportions, with the highest values (92.18%) in the model consisting of fiberglass.The results of the strains and stresses obtained at the seven points and distributed in order at the holes on the surface of the four models located in the path (A -A) most of the increments were at the third point, While the highest value of displacements was at the fifth and eighth points.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.099
GPT teacher head0.285
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes1
Has abstractyes

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