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Record W2552224350 · doi:10.1115/ipc2016-64052

Microstructure Engineering of Thicker Gage Niobium Microalloyed Line Pipe Steel With Enhanced Toughness by High Temperature Processing Using TiN-NbC Composite Precipitate

2016· article· en· W2552224350 on OpenAlexafffund
Xiaoping Ma, Chengliang Miao, Xiaobing Zhang, Laurie Collins

Bibliographic record

VenueVolume 3: Operations, Monitoring and Maintenance; Materials and Joining · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsEVRAZ (Canada)McMaster University
FundersCompanhia Brasileira de Metalurgia e MineraçãoMcMaster University
KeywordsMaterials scienceMicrostructureMetallurgyCharpy impact testFracture toughnessThermomechanical processingMicroalloyed steelToughnessComposite materialBrittlenessNiobiumAustenite

Abstract

fetched live from OpenAlex

Prediction of crack arrestability of higher grade line pipe steel microalloyed with niobium in full scale burst tests based on laboratory simulation tests including Charpy impact, DWTT and CTOD is rendered difficult, as the full scale burst test is found to be far more sensitive to microstructure variables than current laboratory tests. This paper deals with nano-scale TiN-NbC composite precipitate engineering as an alternative approach to strain-induced precipitation of NbC to produce thicker gage plate or coil with enhanced toughness and resistance to ductile fracture propagation of line pipe steel. Microstructure engineering is based on identification of key microstructural parameters to which target properties can be related, and engineer the target microstructure through design of base chemistry and optimization of processing schedules. Nano-scale precipitate engineering based on control of spacing and size of TiN-NbC composite precipitate offers a new approach to achieve excellent strength and toughness (300J at −60C) of line pipe steels through control of target microstructure consisting of: (i) refinement of austenite grain size (under 30 microns) of transfer bar before pancaking, (ii) high volume fraction of acicular ferrite with adequate plasticity to increase resistance to ductile fracture propagation, (iii) high density and uniform dispersion of high angle grain boundaries that arrest micro-cracks to suppress brittle fracture initiation, (iv) less intensity of unfavorable {100}<011> texture component that facilitate the propagation of brittle fracture, (v) suppression of ultra-fine precipitates in the matrix, thereby enlarging plastic zone ahead of the crack tip to blunt the tip of the crack, and (vi) suppression of coarse brittle constituents (carbides or MA products) that initiate brittle fracture. Experimental results are presented on thermo-mechanically rolled X-90 and K-60 that validate the concept of microstructure engineering using TiN-NbC composite precipitate engineering to enhance strength and fracture toughness.

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 categoriesMeta-epidemiology (narrow)
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 score0.009
Threshold uncertainty score1.000

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 head0.188
Teacher spread0.182 · 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.

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

Citations1
Published2016
Admission routes2
Has abstractyes

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