Structural Nature of ZnAl4Cu1 Alloy Plasticity Affected by Various Technological Treatments
Bibliographic record
Abstract
The aim of this work was to analyse the microstructural nature of plasticity in ZnAl4Cu1 alloy and its dependence on the processing technology. The alloy condition was analysed after gravity casting, after forging and after ECAP processing. Two alloys with slightly different compositions were studied. For alloy A, the returnable material from a prominent Zn alloy producer was used. For B alloy input raw materials of relatively high purity were used. Tensile testing showed that in the as-cast alloy tensile strength had relatively low levels up to 211 MPa, and particularly low values of ductility only up to 2.5 % were found in B alloy, which was more polluted. By means of ECAP processing of the as-cast alloy, the tensile strength was improved by 50 % (Rm = 312 MPa). In the case of the purer A alloy the majority of samples improved to level A = 27.9 %. Forging of the as-cast alloy preserved strength on a level similar to the ECAP result, but ductility was improved to the level of 34.4 %, although alloy B had lower purity. Further significant ductility improvement was obtained through ECAP processing to A = 147 % of the as-forged alloy. The microstructure of ZnAl4Cu1 consists primarily of segregated η phase (rich in Zn) and fine eutectoid composed of η and alpha phases segregated mostly in dispersive state, but in places also in lamellar form. Close correlation between microstructure and processing method resulted from our fractographic study. In the case of ECAP processing of the forged state the finest microstructure was achieved, which was accompanied by higher plasticity and also by fine dimples of transcrystalline ductile fracture (DTDF).
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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".