Atomic clusters induced rapid hardening behavior in an early stage of isothermal aging for a high-strength Al alloy produced by laser powder bed fusion additive manufacturing
Bibliographic record
Abstract
Due to the transient interaction between laser and powder and layer-by-layer rapid melting and solidification, laser additive manufacturing-fabricated metal components can exhibit unique microstructure evolution behaviors and strengthening mechanisms that are normally not available in traditional processes. In this work, a previously unreported rapid hardening behavior at the very early stage of isothermal aging for laser powder bed fusion-processed high-strength Al-5024 alloy was revealed. The microstructures and mechanical properties of specimens aged from 10 min to 120h were systematically analyzed. It showed that the specimens underwent two peak hardening processes during an isothermal aging at 325 °C. The mechanical properties of the specimens including microhardness, yield strength, and elastic modulus were significantly enhanced after an extremely short aging time of 10 min and then reached a secondary peak hardening at an aging time of 4h, where the yield strength of 450 ± 10.3 and 463.2 ± 13.2 MPa were obtained, respectively. The unusual aging responses were attributed to the formation and decomposition of Sc-rich clusters with a high number density of 2.7 × 1023 m−3 and nano-size of 2.71 nm. These clusters were characterized by transmission electron microscopy analyses and further supported by differential scanning calorimetry measurements, where a significantly higher activation energy of 147.6 ± 21.1 kJ/mol corresponding to the precipitation/coarsening process of Al3(Sc,Zr) was measured for rapid hardening specimens. In addition, the relationship between the aging process, the evolution of nano-precipitates, and the mechanical properties was systematically demonstrated.
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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".