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Record W4296004383 · doi:10.3390/ma15186331

Mechanical Performance and Corrosion Behaviour of Diffusion-Bonded A5083 Aluminium and A36 Mild Steel with Gallium Interlayer

2022· article· en· W4296004383 on OpenAlexaff
Asmawi Ismail, Nurul Husna Othman, Mazli Mustapha, Mohamed Shuaib Mohamed Saheed, Zaki Abdullah, Musa Muhammed, Asmalina Mohamed Saat, Faizal Mustapha

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversité du Québec à Chicoutimi
FundersMinistry of Higher Education, Malaysia
KeywordsMaterials scienceIndentation hardnessCorrosionScanning electron microscopeSurface roughnessAluminiumMetallurgyComposite materialSurface finishBond strengthDiffusionMicrostructureAdhesiveLayer (electronics)

Abstract

fetched live from OpenAlex

This article investigated the mechanical performance and corrosion behaviour of a diffusion-bonded A5083 aluminium/A36 mild steel dissimilar joint with a Gallium (Ga) interlayer. The bonding parameters were the bonding temperature (525 and 550 °C), holding time (60 and 120 min) and surface roughness (800 and 1200 grit). Property characterisation was achieved using Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX) analysis, Vickers microhardness tester, Izod impact tester and potentiodynamic polarisation testing. The results revealed that the significance of the bonding parameters was in the order bonding temperature > surface roughness > holding time. Increasing the bonding temperature resulted in an increase in the impact strength and a corresponding reduction in the corrosion rate and microhardness. However, increasing the grit size decreased the microhardness and a corresponding increase in the impact strength and corrosion rate. The impact strength and corrosion rate decreased with the increasing holding time while the microhardness followed a reverse trend. It was also discovered that incorporating the Ga interlayer resulted in a 67.9% improvement in the degradation rate.

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 score0.008
Threshold uncertainty score0.466

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)0.0000.000

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 head0.008
GPT teacher head0.208
Teacher spread0.200 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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