MétaCan
Menu
Back to cohort
Record W2502324907 · doi:10.2495/cmem-v4-n3-336-344

Effects of skin thickness and core density on the residual dent depth in aerospace sandwich panels

2016· article· en· W2502324907 on OpenAlexaff
Diane Wowk, Catharine Marsden

Bibliographic record

VenueInternational Journal of Computational Methods and Experimental Measurements · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia UniversityRoyal Military College of CanadaRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsCore (optical fiber)ResidualAerospaceMaterials scienceSandwich-structured compositeComposite materialStructural engineeringEngineeringAerospace engineeringComputer science

Abstract

fetched live from OpenAlex

Sandwich panels are commonly used for aerospace structures that require a high-bending stiffness, but the thin facesheets that are bonded to the core can be susceptible to impact damage. It is necessary to be able to identify and assess the severity of the damage, but this can be difficult when dents are not visible on the surface of the skin. This can occur when the dent elastically springs back immediately after impact, and can cause the skin to return close to its original position, leaving little indication that a damaged core exists. Identifying combinations of skin thickness and core density that are more susceptible to spring back can enable better decisions to be made with respect to inspection procedures. Finite element simulations of metal-skinned honeycomb panels indicate that more spring back is expected to occur from panels composed of thicker skins and lower density core.

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)0.000
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 score0.074
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.366
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Computational Methods and Experimental MeasurementsSame topicMechanical Behavior of CompositesFrench-language works237,207