Diffusion Bonding of Austenitic Stainless Steel 316L to a Magnesium Alloy
Bibliographic record
Abstract
Dissimilar metal combinations are often necessary when manufacturing a component in order to meet particular functional and engineering requirements or protect against environmental degradation. Stainless steels are used in a diverse range of applications due to their excellent corrosion resistance, formability and strength. The 316L stainless steel also shows good crashworthiness due to its high strain rate sensitivity which makes it suitable for the transportation industry. The joining together of the 316L steel and AZ31 magnesium alloy cannot be achieved using conventional fusion welding methods and therefore, diffusion bonding using interlayers was used to overcome the differences in their physical properties. The results show that Cu and Ni interlayers form a eutectic with the magnesium which enhances wettability and bond formation through isothermal solidification. The effect of hold time on the microstructural developments across the joint region was studied at a bonding temperature of 530oC and 510oC for the Cu and Ni interlayers respectively using a bonding pressure of 0.2 MPa. This preliminary investigation shows that by increasing the bonding time from 5 to 60 minutes results in a Cu-Mg and Ni-Mg eutectic phase structure forming along the bond interface. By holding the joint at the bonding temperature for 15 minutes initiates isothermal solidification of the joint and this was confirmed by DSC analysis. However, the movement of the solid/liquid interface on solidification pushes intermetallic phases into the center of the bond during the solidification stage. The intermetallics increase the hardness value of the bond interface and lower final bond strengths.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".