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Record W2999778080

An Investigation into the Austenite Decomposition Behaviour and Post-Forming Tempering Response of Two 1800 MPa Grades of Press Hardening Steels

2019· dissertation· en· W2999778080 on OpenAlexfundno aff
Claire Bourque

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsTemperingAusteniteHardening (computing)MetallurgyMaterials scienceComposite materialMicrostructure
DOInot available

Abstract

fetched live from OpenAlex

In this research, two different 1800 MPa grades of press-hardening steel were investigated; one with 0.30 wt% and the other 0.32 wt% of Carbon. The suitability of an austenite decomposition model after cooling and the effect of short tempering times after quenching on the final mechanical properties was examined. To assess the austenite decomposition model, both materials were subject to a variety of constant cooling and resulted in a mixed-phase of bainite, martensite, and ferrite. The Gleeble experiments were simulated using LS-Dyna and a thermal-mechanical-microstructural model. In the second investigation (Part II – Short Cycle Tempering), both steels were fully quenched in the Gleeble to produce a fully martensitic microstructure and tempered at temperatures ranging from 100 to 700 °C and times of 0.5 to 15 s For both experiments microhardness tests were conducted on the specimens and a FESEM was used to characterize and quantify the resultant mixed-phase microstructures.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes1
Has abstractyes

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