Limitation of Distortion in Friction Stir Welded (FSW) Panels Using Needle Peening
Bibliographic record
Abstract
Friction stir welding (FSWing) induces residual stresses and distortions in welded structures. Such residual stresses reduce the fatigue life of welded components, while the induced distortions prevent the welding of large or thin components. In the present study, needle peening was used to induce additional residual stresses in 2.3-mm thick (FSWed) aluminum alloy (AA) 2024-T3 sheets. This was done with the objective to counterbalance the welding-induced stresses and thus reduce the overall stresses and distortions. The needle peening process, which stems from shot peening, consists of hammering a surface using cylindrical spherical ended shots sliding back and forth in a treatment head. An instrumented needle peening machine was used to carry out peening on as-received (or bare) and bead-on-plate FSWed AA2024-T3 material. In both cases, the width of the peening area corresponded to that of a typical weld. The influence of the peening process parameters such as needle size, applied power and travel speed on the surface quality and magnitude of the induced distortions were evaluated. The results indicate that, by increasing the needle diameter from 1.2 mm to 2.0 mm, the peening-induced deflection on bare sheet material increased by an average value of 27% while the roughness average, Ra, decreased by an average value of 47%. It was also found that a surface finish qualitatively similar to that of conventional shot peening could be obtained by using appropriate needle peening trajectories. Finally, needle peening with an applied power of 10% was sufficient for eliminating 37% of the welding-induced transverse curvature and 82% of the welding-induced longitudinal curvature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".