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Record W1979513938 · doi:10.1002/pen.24042

Effect of tensile loading history on mechanical properties for polyethylene

2014· article· en· W1979513938 on OpenAlexafffund
P.‐Y. Ben Jar

Bibliographic record

VenuePolymer Engineering and Science · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsMaterials scienceCrossheadComposite materialUltimate tensile strengthTensile testingLamella (surface anatomy)ModulusMicrostructureTangent modulusDeformation (meteorology)BendingFlexural strength

Abstract

fetched live from OpenAlex

Influence of loading history on the mechanical properties for polyethylene (PE) was examined through an experimental study, in which each specimen was subjected to two short‐term tensile tests that were conducted at least 1 month apart. The first test was to apply various loading histories to specimens, and the second test to characterize the corresponding change in mechanical properties. The results show clearly that the loading history introduced in the first test affects mechanical properties measured from the second test. In particular, the initial tangent modulus and stress response to deformation from the second test show a noticeable decrease with the increase of the applied strain introduced in the first test, even at the applied strain for the first test as low as 0.05. Change in mechanical properties was also found to depend on loading mode (cyclic and monotonic) and crosshead speed (1 mm/min and 5 µm/min) used in the first test. The above changes are explained based on the microstructural concept of interphase that is located in the interlamellar region and has a less ordered microstructure than the crystalline lamella. Based on results from this study, further investigation is planned to obtain direct evidence to support the idea. POLYM. ENG. SCI., 55:2002–2010, 2015. © 2014 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)0.001
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 score0.033
Threshold uncertainty score0.318

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 head0.014
GPT teacher head0.210
Teacher spread0.195 · 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

Citations10
Published2014
Admission routes2
Has abstractyes

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