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Creep Mechanisms vis-à-vis Power Law vs. Grain Boundary Sliding in α-β Titanium Alloys for Physics Based Prognostics

2015· article· en· W3136134194 on OpenAlexaff
Amar Kumar, Alka Srivastava, Nita Goel, A. K. Banerjee, Ashok K. Koul

Bibliographic record

VenueAnnual Conference of the PHM Society · 2015
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsLife Prediction Technologies (Canada)
Fundersnot available
KeywordsCreepMaterials scienceGrain boundaryGrain Boundary SlidingTitanium alloyPower lawConstitutive equationMATLABStructural engineeringAlloyMechanicsMetallurgyComposite materialComputer scienceEngineeringPhysicsFinite element methodMathematicsMicrostructure

Abstract

fetched live from OpenAlex


 
 
 This work is performed in support of our continued physics-based prognostics system development using a life cycle management-expert system (LCM-ES) framework. The physical damage based modeling approach involving global behavior and localized response of a component at the microstructural level is used. The current research aims at constructing parts of a deformation mechanism map (DMM) for α-β Ti alloy. The appropriate constitutive equations are used for power-law creep and grain boundary sliding mechanisms. Simulations are performed using the Newton- Raphson method using Matlab software code in order to obtain contour lines corresponding to strain rates ranging from 104 to 10-12 over the homologous temperature ranges of 0.10 to 0.655. The dominance of power-law creep and grain boundary sliding over a wider range of stresses and temperatures in Ti-64 alloy is studied. The simulation results are validated using experimental data points. The predicted contour lines in the map match fairly well. The structure- creep mechanism relationships in α-β Ti alloy under different stress, temperature and strain rate conditions are discussed.
 
 

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score0.598

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 head0.031
GPT teacher head0.241
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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