MétaCan
Menu
Back to cohort
Record W3110797303 · doi:10.3390/ma13245801

Post-Weld Heat Treatment of API 5L X70 High Strength Low Alloy Steel Welds

2020· article· en· W3110797303 on OpenAlexaff
Houman Alipooramirabad, Anna Paradowska, Shahrooz Nafisi, Mark Reid, Reza Ghomashchi

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceWeldingMetallurgyUltimate tensile strengthResidual stressDuctility (Earth science)MicrostructureAlloyComposite materialCreep

Abstract

fetched live from OpenAlex

High Strength Low Alloy (HSLA) steels are the materials of choice in pipeline construction with the API X70 grade as the steel for the majority of pipeline networks constructed during the late 20th and early this century. This paper reports on the influence of Post-Weld Heat Treatment (PWHT) on the reduction of residual stresses, resulting changes in the microstructure, and mechanical properties of a multi-pass, X70 HSLA steel, weld joints made by a combined Modified Short Arc Welding (MSAW) and Flux Cored Arc Welding (FCAW) processes. Neutron diffraction results highlighted high magnitude of tensile residual stresses, in excess of yield strength of both parent and weld metal, in the as-welded specimen (~650 MPa), which were decreased substantially as a result of applying PWHT (~144 MPa). Detailed microstructural studies are reported to confirm the phase transformation during PWHT and its interrelationship with mechanical properties. Transmission Electron Microscopy (TEM) analysis showed polygonization and formation of sub-grains in the PWHT specimen which justifies the reduction of residual stress in the heat-treated weld joints. Furthermore, microstructural changes due to PWHT justify the improvement in ductility (increase in the elongations) with a slight reduction in yield and tensile strength for the PWHT weld joint.

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 score0.004
Threshold uncertainty score0.853

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)0.0010.000

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 head0.213
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

Citations21
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMaterialsSame topicWelding Techniques and Residual StressesFrench-language works237,207