Explosive Welding of Thin Aluminum Plate onto Magnesium Alloy Plate Using a Gelatin Layer as a Pressure-Transmitting Medium
Bibliographic record
Abstract
Mg alloys are extensively used in various automotive, aerospace, and industrial applications. Their limited corrosion resistance can be enhanced by welding a thin Al plate onto the alloy surface. In this study, we perform the explosive welding of a thin Al plate, accelerated by the detonation of an explosive through a gelatin layer as a pressure-transmitting medium, onto two Mg alloy samples: Mg96Zn2Y2 alloy containing a long-period stacking ordered phase in an α-Mg matrix and commercial AZ31. The bonding interface is characterized using optical microscopy, scanning electron microscopy, X-ray diffraction, and electron probe microanalysis. Under moderate experimental conditions, the thin Al plates are successfully welded onto the Mg alloys, showing typical wavy interfaces without intermediate layers. Due to the decreased energetic condition corresponding to the use of a thin flyer plate and gelatin medium, the resulting bonding quality is better than that obtained using a regular explosive welding technique. Further, based on the well-known window for explosive welding, we estimate that the experimental conditions for successful bonding are close to the lower welding limit for a thin Al plate with the two Mg alloys considered. These findings may contribute to improving the quality of materials welded with explosive welding.
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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) | 0.001 | 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".