Localization of plastic deformation at weld seams of porthole die Al-Mg-Si extrusions
Bibliographic record
Abstract
This study investigates the localization of plastic strain near the weld seams of porthole die extruded Al-Mg-Si alloys with primarily unrecrystallized microstructures, relevant to post-forming or crash scenarios. A link was established between the crystallographic texture and the plastic response of the material at a local level. Optical metallography and electron backscatter diffraction (EBSD) were used to systematically characterize the microstructure and textures in the extrudates. It was found that a variation in bridge geometry produced very different patterns of textures near the weld seams. To determine the effect of the texture patterns, a slip system level polycrystal plasticity code was used to simulate the mechanical response for each region of similar texture. The predicted properties were then used to fit a Barlat YLD2004-18p anisotropic yield function, and finite element method (FEM) simulations were conducted using the yield functions as inputs. The results revealed a match between the simulated patterns of strain localization and the experimental strain patterns observed via a micro-scale digital image correlation (DIC) technique for each bridge case. These findings establish crystallographic texture as a primary factor affecting the mechanical behaviour of extruded profiles, opening the door to influencing the weld seam properties through the control of crystallographic texture using die bridge design.
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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".