Parametric study to investigate mechanical properties of welded dissimilar Al6063 and Al 7073 alloys through FSW process
Bibliographic record
Abstract
Abstract Aluminium alloys have been the most prominent materials that have applications in every industry due to their high strength-to-weight ratio. The low melting point and easy recyclability also attract the scientific community to apply such material in modern manufacturing. The revolution in aluminium alloys came after the invention of friction stir welding, which provided high-end welding results and catastrophically increased its application in aerospace industries. Still, many inconclusive studies need to be explored for its high-end application, especially for newly invented aluminium alloy composites. Hence, this study investigates the application of friction stir welding process for welding dissimilar Aluminium alloy compositions. The investigation starts by analysing the effect of rotational speed, feed rate, and force on temperature generation, hardness, and welding strength. Three levels of process parameters, i.e. rotational speed in the range of 1000–1200 rpm, force of 12–18 N and feed rate of 40–60 mm min−1, are selected to analyse the effect on hardness and strength of the weld. After analysis, the optimum conditions obtained were a rotational speed of 1200 rpm, a feed rate of 50 mm min−1, and an average load of 15 N for a maximum hardness of 93.16 BHN and welding strength of 228 MPa. The investigation’s findings indicated that several phenomena, including the effects of high blending activity, plastic disfigurement, the repercussions of grain structure, and frictional intensity, may influence the hardness and strength of the weld. The growth of uniform structure at the stirred area is caused by pin movement during welding, specifically from the retreating side to the advancing side.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".