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Record W4385623327 · doi:10.1088/2053-1591/acedec

Effect of microstructural rearrangement as a result of annealing on the corrosion behavior of 7075 aluminum alloy

2023· article· en· W4385623327 on OpenAlexaff
Anawati Anawati, Aisyah Nur Aliyah, Ayoub Tanji, Hendra Hermawan

Bibliographic record

VenueMaterials Research Express · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceCorrosionIntergranular corrosionMetallurgyAnnealing (glass)AlloyDissolutionGrain boundaryAluminium6111 aluminium alloyMicrostructureChemical engineering

Abstract

fetched live from OpenAlex

Abstract Understanding of the relationship between microstructural change and corrosion behavior is essential for achieving the optimum benefit of strengthening precipitates in providing high strength without sacrificing the corrosion resistance of 7000 series aluminum alloys. This work aims at revealing the relationship between the microstructural rearrangement and corrosion behavior of a 7075 series aluminum alloy as a result of annealing at the temperature range of 300 °C–600 °C. Microstructural observation indicated a gradual dissolution of Cu in the solid solution matrix with increasing annealing temperature. At 600 °C, a formation of segregated Al2Cu phase occurred along the grain boundaries. After subjected to corrosion tests, exfoliation corrosion was observed on the as-received specimen, as well as on the specimens annealed at 300, 400 and 500 °C, whilst an intergranular corrosion occurred on that annealed at 600 °C. The corrosion resistance was higher on the specimen annealed at 500 °C compared those annealed at other temperatures, as the result of Cu enrichment the solid solution matrix.

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 score0.003
Threshold uncertainty score0.006

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 head0.035
GPT teacher head0.317
Teacher spread0.282 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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