Microstructure, Texture, Electrical and Mechanical Properties of AA-6063 Processed by Multi Directional Forging
Bibliographic record
Abstract
In current research, the effect of the multi-directional forging (MDF) process on the microstructure, texture, mechanical and electrical properties of AA-6063 under different heat treatment conditions at various MDF temperatures was studied. The annealed AA-6063 alloy was processed up to three passes of MDF at ambient temperature. Three passes of this process were also applied to the solution-treated AA-6063 at ambient temperature and 177 °C. Microstructural investigations demonstrated that the MDF process led to a significant reduction in the average grain size as well as a considerable increase in the fraction of low angle grain boundaries. Texture analysis revealed that copper and Goss textures were mainly developed within the annealed and solution-treated samples of AA-6063, respectively. The hardness and shear strength values of all processed samples also showed a sizeable improvement compared to the initial heat-treated samples. For example, the hardness and shear yield strength value of the solution-treated sample MDFed for three passes showed more than 100 and 70% increase, respectively. The effect of the MDF process on the electrical conductivity of AA-6063 under different heat treatment conditions at various temperatures was negligible. So, it can be concluded that the MDF process increased the mechanical properties without an appreciable decrease in electrical conductivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".