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Record W2066000931 · doi:10.1115/ipc2004-0545

Correlation Between Microstructure and Hardness of the Weld HAZ in Grade 100 Microalloyed Steel

2004· article· en· W2066000931 on OpenAlexaff
Kioumars Poorhaydari, B.M. Patchett, Douglas G. Ivey

Bibliographic record

Venue2004 International Pipeline Conference, Volumes 1, 2, and 3 · 2004
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceMartensiteMicrostructureMetallurgyHeat-affected zoneFerrite (magnet)WeldingGrain sizeIndentation hardnessHardening (computing)Microalloyed steelTransmission electron microscopyComposite materialAusteniteLayer (electronics)

Abstract

fetched live from OpenAlex

The weld thermal cycle results in significant changes in microstructure and, consequently, mechanical properties of the weld heat affected zone (HAZ). In this paper, hardness variations across the HAZ and for different welding heat inputs (0.5–2.5 kJ/mm), obtained in a Grade 100 microalloyed steell, are explained based on the microstructural observations. Micro- and nano-hardness examination provided hardness profiles across the HAZ and nano-distribution of hardness in each HAZ sub-region, respectively. Optical microscopy and transmission electron microscopy (TEM) were used for evaluation of grain size, phase structure and precipitate type, shape and distribution. Both carbon replicas and thin foils (prepared by focused ion beam technique) were used for TEM. The fine-grained HAZ for all heat inputs was primarily composed of polygonal ferrite, with some regions of twinned martensite in the higher heat input (1.5 and 2.5 kJ/mm) samples. Twinned martensite regions were also identified in the coarse-grained HAZ of the 0.5 kJ/mm sample. Grain size changes were the major cause for the variation of hardness in the fine-grained HAZ; however, large packets of bainitic ferrite and martensite in the coarse-grained HAZ, with small ferrite/martensite laths, were responsible for the relative hardening in the coarse-grained HAZ.

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 score0.540
Threshold uncertainty score0.518

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 science0.0000.000
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 head0.012
GPT teacher head0.214
Teacher spread0.202 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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Same venue2004 International Pipeline Conference, Volumes 1, 2, and 3Same topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207