The Effect of Die Bearing Geometry on Surface Recrystallization During Extrusion of an Al-Mg-Si-Mn Alloy
Bibliographic record
Abstract
Abstract The formation of large surface grains known as peripheral coarse grains (PCG) is an undesirable feature commonly observed in extruded medium-strength Al-Mg-Si alloys produced for many applications including automotive. The objective of this study was to evaluate factors contributing to the formation of PCG layers, particularly the die bearing geometry, with the goal of developing strategies to eliminate or reduce this phenomenon. This was accomplished using a combination of extrusion trials and finite element method simulations to characterize the role of die bearing geometry on the formation of surface microstructure during the extrusion of an Al-Mg-Si-Mn alloy. The extrusion trials were conducted using two die bearing geometries, (i) a zero bearing die and (ii) a choke die using an extrusion temperature of 480 °C and ram velocities of 20 to 30 mm/s. Axisymmetric extrusion was conducted with an extrusion ratio of 16.5. During the extrusion trials, partially extruded billets were extracted from the extrusion press and water quenched in order to follow the evolution of the surface microstructure for the different bearing geometries. In addition, ram motion was arrested in the middle of the extruded length, held for 5 seconds and then resumed to investigate the role of changing the deformation conditions of the surface on the extruded microstructure. Optical microscopy and electron back-scattered scanning diffraction (EBSD) were used to quantify the microstructure and crystallographic texture of the extrudates and partially extruded billets at different spatial locations. A finite element (FE) mathematical model using the commercial software package DEFORM 2D was also developed to simulate the extrusion process so that loads, temperatures, and material flow patterns could be predicted. The FE model was used to track material flow streamlines close to the surface. Specific locations along these streamlines were then selected for EBSD analysis on the partially extruded billets. The results indicate that the major factor affecting the formation of the PCG layer is the local stored energy of the near surface material which in turn is a function of the details of the die bearing geometry.
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 | 0.001 |
| 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".