The development of crystallographic texture during porthole die extrusion of Al-Mg-Si alloys
Bibliographic record
Abstract
• The evolution of material during porthole die extrusion is characterized. • Material flow can be categorized into porthole channel center and near die bridge flow. • Combining finite element and polycrystal plasticity simulation, texture is predicted. • Mechanical properties are linked to crystallographic texture evolution. The use of hollow aluminum extrusions in internal combustion engine and battery electric powered vehicles has increased significantly in recent years due to lightweighting considerations. It is of interest to understand the evolution of crystallographic texture from a through-process perspective, since the microstructure and texture of the material have a strong influence on plasticity in the final part. In this research, an Al-Mg-Si alloy with Mn and Cr additions to suppress recrystallization was halted mid-extrusion, and the in-die material was extracted for study. The evolution of textures along finite element method (FEM) predicted streamlines were characterized with electron backscatter diffraction (EBSD). Polycrystal plasticity modelling coupled with the FEM simulated deformation history was implemented to predict texture evolution. Streamlines passing near the center of the portholes exhibited axisymmetric double fiber textures, which rotated following the streamlines before shifting to plane strain textures near the die exit. Closer to the weld seam, shear textures developed. It was found that textures could be predicted for streamlines up to 1.4 mm away from the weld seam, where complex deformation modes, significant increase in strain and the possible intervention of alternative mechanisms such as recrystallization and non-octahedral slip inhibit the accuracy of texture prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".