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Record W2556611986 · doi:10.1115/ipc2016-64238

Characterization of Local Mechanical Properties of X80 Pipeline Girth Welds Using Advanced Techniques

2016· article· en· W2556611986 on OpenAlexaff
Abdelbaset R.H. Midawi, Yuji Kisaka, Emanuel B. F. Dos Santos, A.P. Gerlich

Bibliographic record

VenueVolume 3: Operations, Monitoring and Maintenance; Materials and Joining · 2016
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIndentationMaterials scienceWeldingYield (engineering)Ultimate tensile strengthJoint (building)Composite materialMicrostructureMetallurgyStructural engineering

Abstract

fetched live from OpenAlex

An instrumented indentation technique is proposed as a method to directly measure the local yield strength distribution in each zone of gas metal arc welds produced in X80 linepipe. The joints were produced with different microstructures and mechanical properties by applying shielding gases with varying Ar/CO2 ratios of 50 to 15% CO2 and the addition of a pure titanium wire into the weld pool was used to achieve in-situ alloying. The local yield strength distribution for each weld zone was then measured with instrumented indentation. The mapped yield strength distributions measured by instrumented indentation was compared to the hardness distribution. In addition, the yield strength of each zone obtained by instrumented indentation were then compared to tensile test results from Digital Image Correlation (DIC), in order to obtain stress-strain curves for each microstructural zone of the weld. The yield strength results obtained from both techniques are in good agreement, suggesting that instrumented indentation can be useful method to measure the local yield strengths of specific regions in a welded 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 score0.069
Threshold uncertainty score0.434

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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