Phenomenological studies of hot tearing during solidification of magnesium alloys
Bibliographic record
Abstract
There is a renewed interest in magnesium alloys in the automotive industry. Magnesium alloys are ~35% lighter than aluminum and ~80% lighter than steel. As a result, incorporation of magnesium alloy castings in new vehicles plays a critical role in reducing the overall vehicle weight and increasing the vehicle’s fuel efficiency. Magnesium alloys processed via permanent mold casting (PMC) show a high susceptibility to hot tearing. While several techniques are used to relieve hot tearing (e.g., preheating of molds, grain refinements or elimination of sharp features in part design), the underlying mechanisms responsible for hot tearing remain unclear. In the case of magnesium alloys, limited work has been carried out to advance the fundamental understanding of hot tearing. This research investigated the influence of alloy microstructure, casting solidifications and casting stresses on the onset of hot tearing in AZ91D and AE42 magnesium alloys. A novel approach to determine casting stresses using neutron diffraction was implemented. A custom design permanent mold was used to manipulate the cooling rate of a casting and enusing susceptibility to hot tearing. The results indicate that the mold temperature had a profound influence on the nucleation of hot tears. When the cooling rate of AZ91D casting reached ~15.1 °C/s (210 °C mold temperature), the fraction solids development was sufficiently fast to prevent adequate long-range interdendritic feeding of the casting. As a result, shrinkage porosity formed. Shrinkage porosity forming at a location of a stress concentration provided a nucleation site for a hot tear. The stress required to open a shrinkage pore into a hot tear at the stress concentration was ~8 – 12 MPa. Increasing the mold temperature of 250 °C decreased the cooling rate, improved interdendritic feeding of the casting, decreased solidification shrinkage and decreased the magnitude of tensile stresses developing in the casting. In the case of the AE42 alloy, interdendritic feeding of liquid was hindered by the A1xREY intermetallic compounds blocking the interdendritic paths. Further, the presence of acicular A111RE3 phase pinning the grain boundaries decreased the ductility of the AE42 alloy. As a result, a very slow casting cooling rate (~ 7.7 °C/s) was required to prevent the nucleation of hot tears. For higher cooling rates, hot tears nucleated at locations of stress concentrations and propagated along interdendritic regions and grain boundaries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".