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 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 | 0.000 | 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) | 0.000 | 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".