Solid-state diffusion enhancement of liquid phase joints between austenitic stainless steel alloy and magnesium alloy
Bibliographic record
Abstract
The drive to reduce vehicle emissions and fuel consumption, yet maintain strength has led to the increase in use of austenitic stainless steels and magnesium alloys in the transport industry. Therefore, the bonding together of these dissimilar alloys is of critical importance. In this study, a solid-state diffusion bonding of 316L to Ni interlayer at 900oC preceded a transient liquid phase bonding between AZ31 and the Ni interlayer at 510oC. The results showed that, during the liquid phase bonding, the bonding time controlled the isothermal solidification stage and resulted in a metallurgical bond. A combination of solid-state reaction at the 316L steel-Ni interface and eutectic liquid formation at the AZ31/ Ni gained a 17% increase in the joint shear strength compared to joints produced without the solid-state diffusion-bonding step.
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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".