MétaCan
Menu
Back to cohort
Record W2753241975 · doi:10.1088/1361-665x/aa8b05

Recovery behaviour of shape memory polyurethane based laminates after thermoforming

2017· article· en· W2753241975 on OpenAlexafffund
Shuiliang Wu, Wensen Xu, Ganesh Balamurugan, Michael R. Thompson, Kent E. Nielsen, Frank A. Brandys

Bibliographic record

VenueSmart Materials and Structures · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermoformingShape-memory alloyMaterials sciencePolyurethaneComposite materialShape-memory polymer

Abstract

fetched live from OpenAlex

Abstract Shape memory polymers (SMPs) can be used to produce a new class of decorative films capable of improved formability and shape recovery in polymer laminates, which are increasingly being used for automotive, aerospace, construction and commercial applications. As a relatively new field there is little knowledge on the shape recovery behaviour of laminates with a SMP film and few methods of quantify that behaviour. The influences of different variables that affect the recovery behaviour of thermoplastic shape memory polyurethanes based laminates including ambient temperature (45 °C and 65 °C), material modulus, and adhesive strength were investigated after thermoforming, through both experimental and modelling methods. The empirical model assisted in identifying the contributions of the adhesive to transfer stresses, which dampened the recovery of the laminate with lower shear strength adhesives. Increasing ambient temperature and the film modulus increased both the final angle recovery ratios and recovery rates.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueSmart Materials and StructuresSame topicPolymer composites and self-healingFrench-language works237,207