Effect of different nanoparticles on microstructure, wetting and joint strength of Al–12Si–20Cu braze filler
Bibliographic record
Abstract
The effect of nanoparticle additives (La 2 O 3 , SiC, and ZrO 2 ) on microstructure and brazing characteristics of Al–12Si–20Cu alloy produced by induction melting was studied. The morphology and composition of the samples was studied by x-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analysis. The melting point of the fillers was determined by differential thermal analysis (DTA). Filler wettability was studied in terms of spread ratio (SR) on Al 3003 substrate. The joint strength was assessed by tensile shear study in brazed lap-joints of Al 3003 sheets. The results demonstrated that addition of La 2 O 3 in Al–12Si–20Cu showed best wetting (79.6%) and melting (531.2 °C), while the addition of SiC alloy showed moderate tensile shear strength (79.1 MPa) and lowest wettability (76.34%) among Al–12Si–20Cu–La 2 O 3 and Al–12Si–20Cu–ZrO 2 and Al–12Si–20Cu–La 2 O 3 SiC composites. The addition of ZrO 2 in Al–12Si–20Cu alloy showed moderate wetting (78.2%) and lowest tensile strength (78.3 MPa) compared to Al–12Si–20Cu–La 2 O 3 and Al–12Si–20Cu–SiC. The mechanism behind the microstructural modification of Al–12Si–20Cu alloy in the presence of nanoparticle additives has also been discussed.
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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".