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Record W4399773646 · doi:10.1007/s12289-024-01840-0

Numerical simulation and experimental validation of microstructure evolution during the upsetting process of a large size martensitic stainless steel forging

2024· article· en· W4399773646 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Material Forming · 2024
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsCégep de Sorel-TracyÉcole de Technologie Supérieure
FundersMitacs
KeywordsForgingMicrostructureMaterials scienceVolume fractionMetallurgyDeformation (meteorology)Finite element methodMartensiteStrain rateMartensitic stainless steelComposite materialStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The microstructure evolution, plastic deformation, and damage severity during the open die hot forging of a martensitic stainless steel were investigated using finite element (FE) simulation. A microstructure evolution model was developed and combined with a visco-elastoplastic model to predict the strain, the strain rate, and the temperature distribution, as well as the volume fraction and the size of dynamically recrystallized grains over the entire volume of an industrial size forging. The propensity to damage during hot forging was also evaluated using the Cockcroft & Latham model. The three models were implemented in the FE code and the results analyzed in terms of microstructure inhomogeneity and stress levels in different regions of the forging. A good agreement was obtained between the predicted and the experimental results, demonstrating that the simulation provided a realistic representation of the forging process at the industrial scale.

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 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 score0.040
Threshold uncertainty score0.337

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 communication0.0000.001
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 head0.005
GPT teacher head0.264
Teacher spread0.259 · 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