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Record W1008534606 · doi:10.1520/stp11423s

Modeling Thermomechanical Cyclic Deformation by Evolution of Its Activation Energy

2003· book-chapter· en· W1008534606 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

Venuenot available
Typebook-chapter
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsDeformation (meteorology)Materials scienceComposite material

Abstract

fetched live from OpenAlex

This paper presents a new approach for modeling the deformation response of metallic materials under thermomechanical fatigue loading conditions, based on the evolution of thermal activation energy. In its physical essence, inelastic deformation at high temperatures is a thermally activated process. The thermal activation energy, which controls the time and temperature dependent deformation behavior of the material, generally evolves with the deformation state (γp) of the material, in response to the applied stress τ. In the present approach, the inelastic flow equation is integrated for a deformation range where strain hardening is predominant. The simplified integration version of the model only needs to be characterized/validated by isothermal tensile and fatigue testing, and it offers an explicit description of the TMF behavior in terms of physically defined variables. By identifying the dependence of these variables on the cyclic microstructure, the model may also offer a mechanistic approach for fatigue life prediction.

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)0.000
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: none
Teacher disagreement score0.882
Threshold uncertainty score0.997

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)0.0040.000

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 head0.017
GPT teacher head0.195
Teacher spread0.178 · 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