Precipitation Kinetics and Strengthening of a Fe-0.8wt%Cu Alloy.
Bibliographic record
Abstract
Precipitation kinetics and strengthening have been investigated for a Fe–0.8wt%Cu alloy. Microstructure evolution during aging at 500°C has been studied by a combination of Transmission Electron Microscopy and Small-Angle X-ray Scattering to provide information on the nature and location of the precipitates as well as a quantitative estimate of their size and volume fraction. The associated mechanical properties have been studied by hardness and tensile tests.The precipitation kinetics measured in this study are fully compatible with results reported for alloys with higher Cu levels. Nucleation of Cu precipitates is promoted by the presence of dislocations whereas coarsening rates in the later stages of aging appear to be not affected by fast diffusion paths along dislocations.The strength of individual precipitates increases with precipitate size based on the analysis of the mechanical test results. However, the strength of the largest precipitates observed remains approximately half of the strength required for the Orowan by-passing mechanism. The Russell-Brown model for modulus strengthening has successfully been applied to the current data.Study of the plastic behavior shows that the maximum initial hardening rate is related to the highest strength of the material. This unusual result may be explained by a dynamic strained-induced phase transformation of the precipitates from the bcc to the 9R structure. Consequently, the hardening potential of Fe–Cu alloys is associated with good plastic properties close to peak strength thereby indicating the excellent potential of copper as hardening element for the development of novel high strength interstitial free (IF) steels.
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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".