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Record W7070159307

Prediction of the formation of adiabatic shear bands in high strength low alloy 4340 steel through analysis of grains and grain deformation

2014· dissertation· en· W7070159307 on OpenAlexafffund

Bibliographic record

VenueMspace (University of Manitoba) · 2014
Typedissertation
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdiabatic shear bandGrain sizeMicrostructureDeformation (meteorology)Shear (geology)PlasticityStrain rateShear bandAlloy
DOInot available

Abstract

fetched live from OpenAlex

High strain rate plastic deformation of metals results in the formation of localized zones of severe shear strain known as adiabatic shear bands (ASBs), which are a precursor to shear failure. The formation of ASBs in a high-strength low alloy steel, namely AISI 4340, was examined based on prior heat treatments (using different austenization and tempering temperatures), testing temperatures, and impact strain rates in order to map out grain size and grain deformation behaviour during the formation of ASBs. In the current experimental investigation, ASB formation was shown to be a microstructural phenomenon which depends on microstructural properties such as grain size, shape, orientation, and distribution of phases and hard particles—all controlled by the heat treatment process. Each grain is unique and its material properties are heterogeneous (based on its size, shape, and the complexity of the microstructure within the grain). Using measurements of grain size at various heat treatments as well as dynamic stress-strain data, a finite element model was developed using Matlab and explicit dynamic software LSDYNA to simulate the microstructural deformation of grains during the formation of ASBs. The model simulates the geometrical grain microstructure of steel in 2D using the Voronoi Tessellation algorithm and takes into account grain size, shape, orientation, and microstructural material property inhomogeneity between the grains and grain boundaries. The model takes advantage of the Smooth Particle Hydrodynamics (SPH) meshless method to simulate highly localized deformation as well as the Johnson-Cook Plasticity material model for defining the behavior of the steel at various heat treatments under high strain rate deformation.The Grain Model provides a superior representation of the kinematics of ASB formation on the microstructural level, based on grain size, shape and orientation. It is able to simulate the microstructural mechanism of ASB formation and grain refinement in AISI 4340 steel, more accurately and realistically than traditional macroscopic models, for a wide range of heat treatment and testing conditions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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