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Record W2994842731 · doi:10.3390/met10010029

Heat-Affected-Zone Liquation Cracking in Welded Cast Haynes® 282®

2019· article· en· W2994842731 on OpenAlexfundno aff
Sukhdeep Singh, Joel Andersson

Bibliographic record

VenueMetals · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsnot available
FundersEuropean Regional Development FundGöteborgs UniversitetKnut och Alice Wallenbergs StiftelseChalmers Tekniska HögskolaUniversity of Manitoba
KeywordsLiquationCrackingWeldabilityMetallurgyMaterials scienceGrain boundaryDissolutionHomogenization (climate)Heat-affected zoneWeldingComposite materialChemistryMicrostructure

Abstract

fetched live from OpenAlex

Varestraint weldability testing and Gleeble thermomechanical simulation of the newly developed cast form of Haynes® 282® were performed to understand how heat-affected-zone (HAZ) liquation cracking is influenced by different preweld heat treatments. In contrast to common understanding, cracking susceptibility did not improve with a higher degree of homogenization achieved at a higher heat-treatment temperature. Heat treatments with a 4 h dwell time at 1120 °C and 1160 °C exhibited low cracking sensitivity, whereas by increasing the temperature to 1190 °C, the cracking was exacerbated. Nanosecond ion mass spectrometry analysis was done to characterize B segregation at grain boundaries that the 1190 °C heat treatment indicated to be liberated from the dissolution of C–B rich precipitates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2019
Admission routes1
Has abstractyes

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