MétaCan
Menu
Back to cohort

On preventing HAZ cracking in laser welded DS Rene 80 superalloy

2011· article· en· W2026600336 on OpenAlexaff
L.O. Osoba, R.K. Sidhu, O.A. Ojo

Bibliographic record

VenueMaterials Science and Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLiquationMaterials scienceMetallurgySuperalloyGrain boundaryIntergranular corrosionWeldingHeat-affected zoneAlloyCrackingBrittlenessBorideComposite materialMicrostructure

Abstract

fetched live from OpenAlex

The heat affected zone (HAZ) cracking behaviour in a laser beam directionally solidified (DS) Rene 80 nickel based superalloy subjected to preweld heat treatments was studied. The HAZ cracks in the alloy are grain boundary liquation cracks caused by liquation reaction of both non-equilibrium secondary solidification product, MC carbides and equilibrium solid state reaction product, γ′ precipitates. In contrast to theoretical prediction based a preweld heat treatment that reduced grain boundary liquid film thickness did not result in a lower HAZ cracking, which can be related to concomitant reduction in the ability of the base alloy to relax welding stress. In addition, formation of intergranular M5B3 boride particles in preweld alloy appeared to have aided cracking susceptibility by lowering grain boundary liquation temperature and widening the brittle temperature range in the HAZ during cooling. Based on the analysis of the results, application of a new preweld heat treatment that prevents the formation of the intergranular borides and induces moderate base alloy hardness resulted in a nearly crack free HAZ in laser welded DS Rene 80 superalloy.

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 score0.004
Threshold uncertainty score0.007

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)0.0010.000

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 head0.013
GPT teacher head0.210
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

Citations29
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueMaterials Science and TechnologySame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207