Interfacial Bonding and Mechanical Properties of Al/Mg Dissimilar Refill Friction Stir Spot Welds Using a Grooved Tool
Bibliographic record
Abstract
Al/Mg dissimilar welds were successfully fabricated by refill friction stir spot welding using a grooved sleeve tool. Influences of sleeve penetration depth and rotational speed on the weld formation and mechanical performance were systematically evaluated in terms of welding parameter optimization, interfacial bonding mechanism, hardness distribution and welded joint strength. The results indicated that the success of joining Al alloy to Mg alloy significantly depends on tool sleeve penetration depth. The interfacial bonding mechanism compromised both metallurgical bonding and mechanical inter-locking. Intermetallic compound layers of Al3Mg2 and Al12Mg17 were formed at the Al/Mg interface. The thickness of the intermetallic compound (IMC) layer at the weld center increased from 20–30 μm to 40 μm when the rotational speed increased from 1000 to 2000 rpm. The minimum hardness was 80 HV in Al 7075 and 52 HV in ZEK 100; both were measured in the heat affected zone. The welded joint lap shear strength decreased, and the scatter increased with the increasing of rotation speed, whose maximum was 3.6 kN when the rotational speed was 1000 rpm. In addition, the failure mechanism was determined by tool rotational speed, and found to be interfacial failure under a rotational speed of 1000 rpm and nugget pullout under a rotational speed of 2000 rpm.
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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 | 0.000 | 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".