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Microstructural Observations of Cracking in AA5182 at Semi-Solid Temperatures

2000· article· en· W2040728144 on OpenAlexaff
W.M. van Haaften, W.H. Kool, L. Katgerman

Bibliographic record

VenueMaterials science forum · 2000
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsLarus Technologies (Canada)
Fundersnot available
KeywordsMaterials scienceTearingMetallurgyIngotGrain boundaryCrackingAlloyFracture (geology)Composite materialUltimate tensile strengthCastingSlip (aerodynamics)AluminiumShear (geology)Microstructure

Abstract

fetched live from OpenAlex

Within the general framework of hot tearing research during aluminium DC casting, specimens of alloy AA5182 were loaded in tension and deformed at temperatures between 500 and 580°C and strain rates between 10 -5 - 10 -3 s -1 . At these temperatures the alloy is partially molten and the fraction liquid varies between 0.003 and 0.09. The fracture surfaces of the tensile specimens were studied by SEM. The specimen which cracked at 560°C showed grains covered largely with a smooth layer which was molten at the time of fracture. However, the grain boundary surface was not entirely covered with a liquid film, as ductile fractured solid bridges connecting both sides of the crack were also observed. The fracture surface was further characterised by many small side cracks which are often (re)filled with liquid metal. Where this liquid metal feeding was insufficient a capillary meniscus is present. Further, in-situ observation of crack propagation was done on specimens which were loaded in tension in the SEM at 500°C. These observations showed that fracture occurs almost exclusively along grain boundaries and also showed the occurrence of slip lines. The solid bridges were separated in a very ductile manner. These experimentally induced cracks were compared with hot tears developed in an AA5182 ingot during a casting trial in an industrial research facility.

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 score0.031
Threshold uncertainty score0.548

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)0.0010.000

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 head0.012
GPT teacher head0.221
Teacher spread0.210 · 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

Citations11
Published2000
Admission routes1
Has abstractyes

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