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Record W3165379305 · doi:10.1002/mame.202100165

Universal Strain‐Life Curve Exponents for Thermoplastics and Elastomers under Tension‐Tension and Torsion

2021· article· en· W3165379305 on OpenAlexafffund
Valerian Hirschberg, Lorenz Faust, Denis Rodrigue

Bibliographic record

VenueMacromolecular Materials and Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceBrittlenessTorsion (gastropod)AmplitudeExponentComposite materialElastomerPower lawMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The fatigue behavior of 28 amorphous and semi‐crystalline thermoplastic polymers and elastomers is tested under strain controlled sinusoidal tension‐tension (TT) and torsion (T) at room temperature and analyzed via strain‐life (total strain amplitude versus fatigue lifetime) and crack propagation rate versus total strain amplitude curves, analogous to Paris’ law. Investigating fatigue is extremely time‐ and resources consuming, so universal relationships between the exponents of the strain‐life power‐law and material properties are of high importance. For a brittle failure mechanism (I), the strain‐life curves are found to have fixed exponents of B TT,I = −0.27 and B T,I = −0.22, respectively, while the crack propagation versus strain amplitude in TT has an exponent of m da/dN,I = 4. For ductile failure (II), fixed strain‐life curve exponents in TT of B TT,II = −0.35 and in torsion of B T,II = −0.48 with m da/dN,II = 2.8 are obtained. In torsion, most semi‐crystalline polymers show brittle and ductile failure depending on the applied strain amplitude, so the strain‐life curve exponent changes accordingly. The universal exponents for strain‐life and crack growth‐strain amplitude curves offer a significant simplification to rapidly estimate, predict, and simulate the fatigue behavior of polymers.

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 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 score0.017
Threshold uncertainty score0.518

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 science0.0000.000
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 head0.011
GPT teacher head0.195
Teacher spread0.184 · 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
Published2021
Admission routes2
Has abstractyes

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