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Record W2037692798 · doi:10.1002/pc.20963

Load bearing properties of three‐component polymer composites

2010· article· en· W2037692798 on OpenAlexaff
Ahsan Ahmed, Atef Fahim, Hani E. Naguib

Bibliographic record

VenuePolymer Composites · 2010
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceComposite materialComposite numberFinite element methodStiffnessSandwich-structured compositeWedge (geometry)Load bearingStress (linguistics)Structural engineering

Abstract

fetched live from OpenAlex

Abstract Metal or solid polymer anchors are used as the load transfer components for foam and foam composites when they are used as the structural elements in design. The traditional method of fixation of these components is by fastening and adhesion. In this study, anchors were created in the form of inserts and were imbedded in the sandwich composite during the foaming process, resulting in the manufacture of three‐component composite. Flexure tests were conducted to study the effect of different geometries (rectangular, cylindrical, tapered/wedge shaped) and lengths of metal inserts on the strengths of sandwich composites. The stress strain response, mode of fracture of sandwich composite with metallic anchors was elucidated. The results showed that long tapered inserts imbedded in sandwich composite provide better load bearing and adhesion properties than other geometries. A model is presented that describes the relationship between stiffness reduction and progressive crack length of sandwich composite. Finite element analysis (FEA) of the interactions between the inserts and sandwich composites under different loads were carried out. FEA modeling and experimental results were in good agreement, thus validating the model. POLYM. COMPOS., 31:1731–1737, 2010. © 2010 Society of Plastics Engineers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
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 science0.0010.000
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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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
Published2010
Admission routes1
Has abstractyes

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