Bulk grain boundary decoration and functionally-graded alloy developed by one-step friction stir process: Experiment and atom probe tomography analysis
Bibliographic record
Abstract
Conventionally-manufactured lightweight metal castings typically exhibit low-strength that can be improved by grain-refinement. Meanwhile, refined-grain materials often undergo grain-growth at elevated or even room temperature, degrading mechanical/functional properties. Current thermodynamic grain-stabilizing approach is either limited to a thin-film-scale or relies on a two-step mechanical-alloying and heat-treatment process. To circumvent these limitations, this study proposes using friction stir-processing (FSP) to develop bulk grain-boundary (GB)-decorated nanograined materials. We hypothesized that the high strain/strain-rate during FSP produces grain-refinement, while the temperature-rise simultaneously drives solute to the solvent-GBs. Developed GB-segregation map for aluminum-Al solvent identifies magnesium-Mg as a suitable solute-element that will segregate at Al-GBs. Two different FSP traverse speeds—low-432 mm/ min and high-1040 mm/ min—at a fixed rotational speed were examined on an Al-Mg-Al sandwich configuration that produces microscale-functionally-graded materials (FGMs). Lower traverse-speed promotes higher heat-input, material flow, microcracks formation, mixed zones, and mechanical hooks near the Al-Mg interfaces, while higher traverse-speed results in lower heat-input, crack-free Al-Mg interfaces, mechanical mixing, and isolated-mixed zones. Using atom-probe-tomography (APT), a nanoscale functionally-graded intermediate region constituting solid-solution , GB-segregation , β-phase , and γ -phase is observed towards the Al-Mg-interface. APT also confirms how the β-phase might have evolved via a spinodal decomposition phenomenon in support of evidence of a miscibility gap in the Al-Mg alloy that was reported about four decades ago. This work offers innovative materials processing that opens new possibilities for structural use of bulk stable-nanocrystalline materials and FGMs.
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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".