Corrosion Fatigue Behaviour of Aluminium 5083-H111 Welded Using Gas Metal Arc Welding Method
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".