Monotonic and Fatigue Response of Heat-Treated Friction Stir Welded Al 6061-T6 Joints: Testing and Characterization
Bibliographic record
Abstract
ABSTRACT The present study evaluates the influence of post-weld heat treatment on the fatigue strength of friction stir welded aluminum 6061-T6 joints. The solution-treatment artificial aging (STA) was applied to the friction stir welding samples prior to cyclic tests. Hardness distributions along the centerline of the welded specimens as well as the tensile properties and microstructural features were initially examined. The stress-controlled cyclic tests were then conducted at constant amplitude loading with the load ratio of R = 0.1. Experimental results revealed that tensile strength of STA heat-treated samples increased 73 % as compared to the as-welded (AW) joints. Furthermore, the STA heat-treated specimens experienced 28 % higher elongation than those of AW samples. A 3D finite element analysis was developed to (i) simulate residual stress distribution of the welded joints after heat treatment processing and (ii) analyze stress/strain components at weld toe induced as a result of applied loading cycles. The heat-affected zone with critical local stress/strain values was found to be the most vulnerable zone for cracking. Predicted fatigue lives by means of the Smith, Watson, and Topper model were found in close agreement with those experimentally obtained values.
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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".