Interfacial microstructure, element diffusion, mechanical properties and metallurgical bonding mechanism of 316L-AlSi10Mg multi-material parts fabricated by laser powder bed fusion
Bibliographic record
Abstract
This work explored the interfacial microstructure, element diffusion, mechanical properties and metallurgical bonding mechanism of 316L-AlSi10Mg multi-material parts fabricated by laser powder bed fusion (LPBF). Experimental results revealed that insufficient volumetric energy density (VED) of the laser caused lack-fusion porosity in the 316L-AlSi10Mg transition zone, while too high VED produced keyhole-induced porosity defects. Using the optimal process parameters, multi-material parts can be produced with a good interface metallurgical bonding without significant defects. The partial Fe-FCC phase in 316L stainless steel changed into the Fe-BCC structure, and this shift has also changed the preferred orientation of the grains. The intermetallic compound Al5Fe2 and AlFe phases were found in the transition zone. In addition, Al-Fe icosahedral quasicrystals with five-fold symmetry were found at the boundary of the molten pool, which was caused by an extremely high cooling rate. The tensile strength of 316L-AlSi10Mg specimens is higher than that of AlSi10Mg but lower than that of 316L. In contrast to the 316L and AlSi10Mg regions, the fracture mechanism of multi-material fusion zone exhibits a quasi-cleavage fracture mode. The Vickers microhardness of the Al-Fe interface zone was higher than that of 316L with an average value of 235.57 HV0.2 and AlSi10Mg with 124.59 HV0.2, and the interfacial maximum hardness reached 526.68 HV0.2, which was caused by the very hard intermetallic compound Al5Fe2 and AlFe. The metallurgical bonding mechanism of multi-materials was that the dissimilar metals were mixed and in-situ alloyed in the molten pool by the Marangoni convection-induced strong circular flow during LPBF processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".