The shearable–non-shearable transition in Al–Mg–Si–Cu precipitation hardening alloys: implications on the distribution of slip, work hardening and fracture
Bibliographic record
Abstract
A systematic study has been conducted to evaluate the nature of the dislocation–precipitate interaction and its relationship to the mechanical properties for a commercial Al–Mg–Si–Cu alloy. A variety of experimental techniques employed including transmission electron microscopy, slip line observations and macroscopic work hardening behaviour. The results from this work indicate that a clear transition in macroscopic behaviour of the alloy can be observed when the precipitates become large enough so that they are not sheared by dislocations. Direct observations using a transmission electron microscope (TEM) indicate that the precursor to the Q phase becomes impenetrable to dislocations when its equivalent diameter is above 2.5–3.0 nm. The transition from shearable to non-shearable precipitates manifests itself in a number of ways including: (i) a change in the local distribution of slip from a banded to a more homogeneous structure and (ii) a characteristic change in macroscopic work hardening behaviour. In addition, observations on intergranular fracture suggest that the distribution of slip and the intrinsic fracture properties of the grain boundary are critical in controlling this process. Finally, an integrated view of the relationship between the basic dislocation–precipitate interaction and the global response of the alloy is rationalized.
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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 | 0.001 | 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".