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Record W2013733665 · doi:10.1155/2014/870649

Unit Cell Analysis of the Superelastic Behavior of Open-Cell Tetrakaidecahedral Shape Memory Alloy Foam under Quasi-Static Loading

2014· article· en· W2013733665 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials Research · 2014
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsShape-memory alloyPseudoelasticitySMA*Materials scienceComposite materialMicrostructureMetal foamFinite element methodPorosityWork (physics)Structural engineeringMechanical engineeringComputer scienceMartensiteEngineering

Abstract

fetched live from OpenAlex

Cellular solid materials and, more specifically, foams are increasingly common in many industrial applications due to their attractive characteristics. The tetrakaidecahedral foam microstructure, which can be observed in many types of foams, is studied in the present work in association with shape memory alloys (SMA) material. SMA foams are of particular interest as they associate both the shape memory effect and the superelasticity with the characteristics of foam. A Unit Cell Finite Element Method approach is used, an approach that allows accurate predicting of the macroscale response of the foam with a highly reduced numerical effort. The tetrakaidecahedral foam’s responses, both in the elastic and in the superelastic stages, are then extracted and compared with results from the literature. The tetrakaidecahedral geometry is found to be of particular interest when associated with SMA as it takes more advantage of the superelastic property of the material than foams with randomly distributed porosity.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.033
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.311
Teacher spread0.270 · 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