Nano-additive manufacturing of multilevel strengthened aluminum matrix composites
Bibliographic record
Abstract
Abstract Nanostructured materials are being actively developed, while it remains an open question how to rapidly scale them up to bulk engineering materials for broad industrial applications. This study propose an industrial approach to rapidly fabricate high-strength large-size nanostructured metal matrix composites and attempts to investigate and optimize the deposition process and strengthening mechanism. Here, advanced nanocrystalline aluminum matrix composites (nanoAMCs) were assembled for the first time by a novel nano-additive manufacturing method that was guided by numerical simulations (i.e. the in-flight particle model and the porefree deposition model). The present nanoAMC with a mean grain size <50 nm in matrix exhibited hardness eight times higher than the bulk aluminum and shows the highest hardness among all Al–Al 2 O 3 composites reported to date in the literature, which are the outcome of controlling multiscale strengthening mechanisms from tailoring solution atoms, dislocations, grain boundaries, precipitates, and externally introduced reinforcing particles. The present high-throughput strategy and method can be extended to design and architect advanced coatings or bulk materials in a highly efficient (synthesizing a nanostructured bulk with dimensions of 50 × 20 × 4 mm 3 in 9 min) and highly flexible (regulating the gradient microstructures in bulk) way, which is conducive to industrial production and application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".