Reduction of Hot Tears: Alloy and Casting Process Optimization Using Neutron Diffraction
Bibliographic record
Abstract
The continued need of vehicle weight reduction provides impetus for research into the development of novel automotive casting alloys and their processing technologies. Where possible, ferrous components are being replaced by aluminum (Al) and magnesium (Mg) alloy counterparts. This transition, however, requires a systematic optimization of the alloys and their manufacturing processes to enable production of defect-free castings. In this context, prevention of hot tears remains a challenge for Al and Mg alloy thin-wall castings. Hot tears form in semi-solid alloy subjected to localized tensile stress. Classical methods of stress measurement present numerous experimental limitations. In this research, neutron diffraction (ND) was used as a novel tool to obtain stress maps of castings and to quantify the effect of two processes used to eliminate hot tears in permanent mold castings: 1) increasing of the mold temperature during casting of Mg alloys, and 2) grain refinement of Al alloys. In case of the AZ91D Mg alloy, the ND results indicate that increasing the mold temperature effectively decreased the level of tensile strain in a casting region with a geometric stress concentration, thereby preventing nucleation of hot tears. In the case of the B206 Al alloy, hot tearing was alleviated by the addition of 0.05 wt% Ti-B grain refiner and the ND mapping revealed a decrease in the stress level along with enhanced stress uniformity across the casting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".