Effect of friction stir welding parameters on microstructure and mechanical properties of the dissimilar alloys of AZ91D and AA7075
Bibliographic record
Abstract
Friction stir welding is an efficient joining process for light metal alloys, offering benefits such as high joint efficiency and reduced grain sizes. This work is to investigate the process parameters that can improve the welding quality and reduce the brittle intermetallic compounds, including Al 3 Mg 2 (β) and Al 12 Mg 17 (γ) in the welded region. It was perceived that the process parameters influenced the morphological characteristics and input heat, thereby affecting the materialization of intermetallic compounds. The primary diffraction peaks of both Al and Mg were present in all the joints. Two specific intermetallic phases, Al 3 Mg 2 and Al 12 Mg 17 , were formed as a result of the diffusion of Al and Mg atoms during welding. These intermetallic compounds were found in all the joints. The Al 12 Mg 17 phase, when mixed with the Mg phase, tended to migrate towards the weld surface where the AZ91D plate was positioned. This migration might lead to constitutional liquation, which refers to the melting of specific phases during solidification, resulting in the materialization of eutectic microstructures. The highest ultimate tensile strength (UTS) value of 116.64 MPa was achieved with a rotational tool speed of 800 r/min and a traverse speed of 20 mm/min. On the other hand, the lowest UTS value of 68.32 MPa was observed with a rotational tool speed of 400 r/min and a traverse speed (TS) of 20 mm/min. It was noted that lower UTS values were associated with the materialization of dense layers of intermetallic compounds (Al 12 Mg 17 and Mg + Al 12 Mg 17 ) in the stir zone when using lower rotational tool speed and TS. It suggests that improper processing parameters can lead to the materialization of intermetallic compound in the stir zone, which subsequently decreases the mechanical properties of the weldments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".