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Record W2157241506 · doi:10.5772/25991

Corrosion Fatigue Behaviour of Aluminium 5083-H111 Welded Using Gas Metal Arc Welding Method

2011· book-chapter· en· W2157241506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsCanadian Society of Intestinal Research
Fundersnot available
KeywordsMaterials scienceMetallurgyCorrosionWeldingAluminium5005 aluminium alloyFriction stir welding6063 aluminium alloyAluminium alloyCorrosion fatigueWeldability6111 aluminium alloyOxideComposite material

Abstract

fetched live from OpenAlex

Aluminium and its alloys are widely used as engineering materials on account of their low density, high strength-to-weight ratios, excellent formability and good corrosion resistance in many environments. This investigation focused on one popular wrought aluminium alloy, namely magnesium-alloyed 5083 (in the strain hardened -H111 temper state). Aluminium alloy 5083 is one of the highest strength non-heat treatable aluminium alloys, with excellent corrosion resistance, good weldability and reduced sensitivity to hot cracking when welded with near-matching magnesium-alloyed filler metal. This alloy finds applications in ship building, automobile and aircraft structures, tank containers, unfired welded pressure vessels, cryogenic applications, transmission towers, drilling rigs, transportation equipment, missile components and armour plates. In many of these applications welded structures of aluminium are exposed to aqueous environments throughout their lifetimes. Welding is known to introduce tensile residual stresses, to promote grain growth, recrystallization and softening in the heat-affected zone, and to cause weld defects that act as stress concentrations and preferential fatigue crack initiation sites. Fatigue studies also emphasised the role of precipitates, second phase particles and inclusions in initiating fatigue cracks. When simultaneously subjected to a corrosive environment and dynamic loading, the fatigue properties are often adversely affected and even alloys with good corrosion resistance may fail prematurely under conditions promoting fatigue failure. The good corrosion resistance of the aluminium alloys is attributed to the spontaneous formation of a thin, compact and adherent aluminium oxide film on the surface on exposure to water or air. This hydrated aluminium oxide layer may, however, dissolve in some chemical solutions, such as strong acids or alkaline solutions. Damage to this passive layer in chloride-containing environments (such as sea water or NaCl solutions), may result in localised corrosive attack such as pitting corrosion. The presence of corrosion pits affects the fatigue properties of the aluminium alloys by creating sharp surface stress concentrations which promote fatigue crack initiation. In welded structures, pits are often associated with coarse second phase particles or welding defects

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.262
Teacher spread0.209 · 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