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Record W4405821657 · doi:10.1177/02670836241309815

Crystallographic texture before and after post weld heat treatment of high-frequency electric resistance welded API X65 linepipe

2024· article· en· W4405821657 on OpenAlexaff
Kopparthi Ravikiran, Leijun Li, Greg Lehnhoff, Yiyu Wang

Bibliographic record

VenueMaterials Science and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsEVRAZ (Canada)University of Alberta
Fundersnot available
KeywordsMaterials scienceWeldingTexture (cosmology)MetallurgyImage (mathematics)Artificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The microstructure and crystallographic textures of API X65 grade linepipe steel were studied in the base metal, the weld interface after high-frequency electric resistance welding (HF-ERW), and the weld interface following post-weld heat treatment (PWHT). Optical microscopy, scanning electron microscopy (SEM), and electron-backscattered diffraction (EBSD) were used to study the microstructure and texture evolution and correlated with Charpy V-notch impact toughness. The Charpy values were 172.9 ± 5.2 J for the base metal, 7.5 ± 1.6 J for the as-welded weld interface, and 69 ± 24 J after PWHT, with the latter still significantly lower than the base metal. Base metal showed a low fraction of low-indexed cleavage planes at about 9% and major texture components were (113)[110], (112)[110], and (332)[113]. In the as-welded condition, major intensities of rotated Cube (001)[110] and Goss (110)[001] texture components were observed near the weld interface. Although PWHT reduced the texture intensities, rotated Cube and Goss components were still observed. The fraction of cleavage planes was about 44% for the as-welded and about 30% for the PWHT-ed weld interface. Clearly, PWHT has reduced but not eliminated the detrimental rotated Cube and Goss textures at the weld interface. These detrimental textures likely have contributed to the low Charpy toughness after welding and continuing after PWHT.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.180
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
Published2024
Admission routes1
Has abstractyes

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