Comparative Study of Fatigue Properties in Friction Stir Welded Dissimilar Aluminium Alloys (AA5083-H111 and AA6061-T6) Using an Optimized Tool Pin Profile Versus a Standard Design Tool
Bibliographic record
Abstract
Friction stir welding (FSW) is a solid-state joining technique particularly effective for welding dissimilar aluminium alloys, such as AA5083-H111 and AA6061-T6.While the influence of FSW tool design on joint properties is acknowledged, the detailed comparative analysis of fatigue properties using different tool designs remains underexplored.This study fills this gap by examining the effects of a standard tool and an innovative tool design with longitudinal cylindrical grooves and circular grooves on the tool shoulder that are compatible with the thickness of the alloy to be welded.The tool designs were optimized using a systematic Design of Experiments (DOE) approach, focusing on enhancing mechanical properties and fatigue life through controlled tool features and process parameters.The tensile strength of the welded joints was quantitatively assessed, revealing that joints produced with the novel tool (S1) achieved a significantly higher tensile strength of 317 MPa (±15 MPa), compared to 285 MPa (±13 MPa) for the standard tool (S2).This improvement is linked to the novel tool's optimized heat generation and material flow characteristics.Moreover, the microstructure and hardness across the weld zones were evaluated to further understand the impact of tool design on the welding outcomes.Fatigue tests were conducted on samples from both tool types at seven different stress levels.The fatigue life, represented through S-N curves, showed that at an applied stress of 160 MPa, specimens welded with the novel tool withstood up to 9,328,980 cycles before failure, surpassing the 7,589,146 cycles endured by the standard tool, indicative of superior fatigue resistance.These results highlight the critical role of tool design in enhancing the FSW process for dissimilar aluminium alloys, demonstrating both the scientific rigor and innovative scope of the study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".