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Record W2947067811 · doi:10.1299/transjsme.19-00058

Computational study on temperature-dependencies of relaxation and elastic properties in anisotropic viscoelastic constitutive law for fiber-reinforced thermoplastic resin

2019· article· en· W2947067811 on OpenAlexaff
Koji Yamamoto, Y. Ishibashi, Masato SOMEMIYA, Norio HIRAYAMA, Kenjiro Terada

Bibliographic record

VenueTransactions of the JSME (in Japanese) · 2019
Typearticle
Languageen
FieldEngineering
TopicComposite Material Mechanics
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsViscoelasticityMaterials scienceConstitutive equationComposite materialThermoplasticAnisotropyRelaxation (psychology)Structural engineeringEngineeringFinite element methodPhysics

Abstract

fetched live from OpenAlex

With use of the numerical material testing (NMT) approach based on homogenization theory, a time-temperature reduction law is proposed for both relaxation and elastic properties in the orthotropic viscoelastic constitutive equation, which is supposed to represent the macroscopic material behavior of unidirectional fiber reinforced thermoplastics (FRTP) under various temperature environments. We start with dynamic viscoelastic measurements for polycarbonate (PC) under different ambient temperatures to evaluate the master curves of storage and loss moduli as well as loss tangent that exhibit the temperature- and time-dependent characteristics of its relaxation and elastic properties. Then, with the use of the obtained viscoelastic properties of the resin, a series of NMTs simulating macroscopic stress relaxation are conducted on a periodic microstructure (unit cell) model of the FRTP to characterize the macroscopic viscoelastic material behavior. Finally, the shift functions of the orthotropic relaxation and elastic properties in the assumed macroscopic viscoelastic constitutive law involving the proposed time-temperature reduction law are determined to clarify its time- and temperature-dependent characteristics.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.421

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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