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Record W4405360287 · doi:10.1115/ipc2024-122652

Microstructural Characterization of CGHAZ of HSLA Steel Welded by a Hybrid of Semi-Automatic Waveform Controlled GMAW and Mechanized FCAW-G Welding Process

2024· article· en· W4405360287 on OpenAlexaff
Junfang Lu, Peter Schamuhn Kirk, Douglas G. Ivey, Bob Huntley, Andy Duncan

Bibliographic record

VenueVolume 3: Operations, Monitoring, and Maintenance; Materials and Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsUniversity of AlbertaSGS (Canada)
Fundersnot available
KeywordsWeldingGas metal arc weldingMaterials scienceWaveformMetallurgyProcess (computing)Characterization (materials science)Heat-affected zoneEngineeringComputer scienceElectrical engineeringVoltageNanotechnology

Abstract

fetched live from OpenAlex

Abstract The semi-automatic waveform-controlled gas-metal arc welding (GMAW) and mechanized flux-cored arc welding (FCAW) processes are commonly used in mainline tie-in welding. Like mainline girth weld construction by the mechanized GMAW process, to reduce the construction cost and minimize unnecessary weld repairs, the fitness-for-service (FFS) approach using Engineering Critical Assessment (ECA)-based weld flaw acceptance criteria were explored for mainline girth weld tie-in welding. The welding, mechanical testing and ECA details were presented in paper IPC2022-86737 for high strength low alloy (HSLA) Grade 483 steels using a hybrid of semi-automatic waveform-controlled GMAW and mechanized gas-shielded FCAW (FCAW-G) processes. This paper is a continuation of that previous work. It is focused on further studies of the specimens from the welding procedure qualification test welds where low fracture toughness values were associated with crack tip opening displacement (CTOD) pop-ins. In this paper, the CTOD pop-in fracture regions were characterized using optical metallography (OM), macro- and micro-scale hardness testing, scanning electron microscopy (SEM) and transmission/scanning transmission electron microscopy (TEM/STEM). From the fractography and detailed post-test metallographic analysis, cleavage crack initiation was observed in the coarse-grained HAZ (CGHAZ). The CGHAZ microstructural zones were large enough to be effectively sampled for hardness testing; however, the suspected brittle phases within the CGHAZ were too small to be assessed with available hardness testing equipment. From metallography examinations using SEM and TEM/STEM, irregular Fe3C particles were identified through carbon extraction replicas and focused ion beam (FIB) samples of the prior austenite grains (PAG) and prior austenite grain boundaries (PAGB) of the CGHAZ. In the FIB samples, twinned martensite (M) and retained austenite (RA) were also identified within the PAG and along the PAGB of the CGHAZ, confirming the presence of MA. The formation of these brittle phases in the HAZ of the HSLA steel is explored and their effect on the low fracture toughness of the HAZ is discussed. This investigation indicates that the inter-critically reheated CGHAZ (ICR-CGHAZ) region is considered to be responsible for the low fracture toughness. An important finding is presented related to the use of heat-tinting and its adverse effect on detailed post-test characterization of CGHAZ/ICR-CGHAZ regions in CTOD specimens. It is also important to mention that it is difficult to precisely identify the region that triggered cleavage fracture or a pop-in without extensive fractography. For pop-ins in the ICR-CGHAZ region due to suspected M-A secondary microphases, it is much more challenging to find the conclusive contributing factor or direct cause because of the effect of multiple thermal cycles of welding, phase transformation during and after welding, the inhomogeneous nature of the weld and HAZ, and the amount and morphology of the local microphases triggering the initiation of a fracture event.

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 score0.011
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.218
Teacher spread0.213 · 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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