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Residual Stress Analysis of A362 Aluminum Alloy Gear Case Using Neutron Diffraction

2018· article· en· W2906466121 on OpenAlexaff
D. Sediako, Joshua Stroh, Alexandra McDougall, Ermia Aghaie

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceNeutron diffractionResidual stressAlloyEutectic systemStress corrosion crackingMetallurgyMetallographyMicrostructureCasingStress (linguistics)DiffractionComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Mercury Marine has used a new alloy, Mercalloy A362, for the manufacturing of a re-designed lower unit transmission gearcase. The enhanced strength of the alloy allowed for a substantial weight reduction in the new design. The purpose of this study was to examine and determine why cracking may develop in the gear casing during in service testing. Two types of material states, (i) as cast and (ii) heat treated were compared. Metallography and neutron diffraction analysis was carried out at locations identified as being areas of high stress by Magma software – which was performed in a separate study. Microstructural characterization at these locations revealed microstructural and the compositional differences. Differences in the porosity, eutectic phase, and volume fraction of the precipitates were observed at various locations of interest in each material state. The residual stress analysis was performed with application of neutron diffraction and revealed that the stresses in the as-cast component reached a maximum value of 120 MPa, which is below the yield strength of the alloy. The heat treatment applied to the castings reduced the stress by approximately 50 MPa. Based on the microstructure and neutron diffraction results, it is likely that performing a heat treatment process extends the lifetime of the component, however, it may not completely eliminate the cracking problem. Farther studies are currently nearing completion, targeting the mass production of the redesigned gearcase.

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 score0.003
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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