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Evaluation of 22MnB5 Steel Austenitization Sub-Models for Simulating the Heating Phase of Hot Stamping

2020· article· en· W3109253588 on OpenAlexaff
Boxuan Zhao, C. I. CHIRIAC, Kyle J. Daun

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMetal Alloys Wear and Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHot stampingMetallurgyMaterials sciencePhase (matter)StampingAusteniteNuclear engineeringMicrostructureEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract The heating phase of hot stamping is the most time-and energy-intensive part of the process, and therefore offers significant potential to improve process efficiency. This requires a robust thermometallurgical model that predicts the blank heating profile and the austenitization progress inside a roller hearth furnace. In this paper, an IM austenitization model for 22MnB5 steel is derived using dilatometry data and evaluated against a JMAK type model using temperature profiles of coupons heated within a laboratory-scale muffle furnace and blanks heated within a roller hearth furnace. The austenitization rates predicted by the two models are then compared to the inferred austenitization processes from those temperature profiles. The result shows that the IM model has potential to capture the trend of austenitization process, especially the rapid pearlite dissolution at the beginning of the transformation, whereas the JMAK type model is too restrictive and fails in this regard.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.232
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.121
GPT teacher head0.298
Teacher spread0.177 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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