Investigation of the microstructural, mechanical, and thermal evolution of dissimilar aluminium alloys during friction stir welding
Bibliographic record
Abstract
This work deals with the joining of 6 mm thick dissimilar aluminium plates (AA5083-H111 and AA6082-T6) using a friction stir welding method and by varying the process parameters. Test experiments were performed to identify the influence of process parameters on the joint efficiency of the weldments. The process parameters such as tool rotation speed and tool pin profile were varied; whereas, tool travel speed, tilt angle, and axial force were kept constant for all weldments. Microstructure evaluation was carried out using light optical and scanning electron microscopes, which exposed the grain refinement in the nugget zone (NZ) and thermo-mechanical affected zone (TMAZ). Mechanical property tests for tensile strength, hardness, and bending were performed to understand the influence of the parameters over the weldments. Heat development between the tool shoulder and workpiece was analyzed by calculating the heat flow and heat flux. The thermal diffusivity of AA5083-H111 and AA6082-T6 were calculated to understand the influence of heat distribution in the joint efficiency of the weldments. It is inferred from the current study that the threaded cylinder tool at a rotational speed of 900 rpm achieved the highest tensile strength, hardness, and bend strength over the combination of other parameters.
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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) | 0.000 | 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 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".