Prediction and Measurements of Thermal Residual Stresses in AA2024-T3 Friction Stir Welds as a Function of Welding Parameters
Bibliographic record
Abstract
Friction Stir Welding (FSW) induces thermal residual stresses resulting in distortions in thin-walled structures. In order to understand and quantify this phenomenon, simulations and experiments of FSW on aluminium alloy (AA) 2024-T3 have been performed using different rotational and welding speeds. A sequentially coupled finite element (FE) model was used to study the residual stresses caused by the thermal cycling induced from FSW. The 3D FE model used temperature-dependent mechanical and thermophysical material properties. The predicted longitudinal stresses peaked at ~300 MPa and had a ‘‘W’’ profile with tensile stress peaks in the weld and compressive stresses outside the weld. In the FE model, the influence of process parameters on residual stress distribution was studied. The application of ‘hot’ welding conditions, i.e. low welding speed and high rotational speed, increased the residual stresses significantly, mainly in the transverse direction. Conversely, ‘cold’ welding conditions resulted in lower residual stresses. The magnitude and distribution of the residual stresses predicted by the FE model were validated by neutron diffraction. The results indicate a good agreement between the measured and predicted residual stresses in AA2024-T3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".