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Record W2973308519

A Microstructural and Damage Investigation into the Low Temperature Impact Behavior of an HSLA Steel

2019· dissertation· en· W2973308519 on OpenAlexaboutno aff
Siyu Wu

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceMetallurgyComposite material
DOInot available

Abstract

fetched live from OpenAlex

Stelco, a Canadian steel making company produced a hot rolled coil of a G40.21 50WT High Strength Low Alloy (HSLA) steel. The material needed to meet a demanding Drop Weight Tear Test (DWTT) specification at -35℃. The coils produced using a coiling temperature of 540℃ did not meet the DWTT specification with mixed pass/fail results. A second coil produced using a coiling temperature of 500℃ met the DWTT specification, but still with mixed results. To investigate this, a two-part study was undertaken. The first part investigated the effect of coiling temperature on the microstructure and CVN impact behavior. It was found that 540℃ coiling temperature resulted in a coarser grain size but less initial void/inclusion content. Furthermore, CVN testing showed that the material coiled at 540℃ had better impact toughness. The second part investigated the mixed pass/fail results for the DWTT and CVN tests conducted at -35℃ for the material coiled at 500℃. The results showed that there were no obvious differences between the pass and failed specimen in terms of microstructure, initial porosity, damage evolution and impact testing behavior, which suggests that the test temperature of -35℃ was within the ductile-brittle transition of this material.

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 score0.633
Threshold uncertainty score0.957

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 science0.0010.000
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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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
Published2019
Admission routes1
Has abstractyes

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