Bibliographic record
Abstract
Current vehicles use mostly ferrous components for structural applications. It is possible to reduce the weight of the vehicle by substituting these parts with those made from light metals such as aluminum and magnesium. Many alloys and manufacturing processes can be used to produce these light metal components and casting is known to be most economical. One of the high integrity casting processes is permanent mold casting which is the focus of this research report. Many aluminum alloy castings used in automotive applications are produced by the sand casting process. Also, aluminum-silicon (Al-Si) alloys are the most widely used alloy systems for automotive applications. It is possible that by using high strength aluminum alloys based on an aluminum-copper (Al-Cu) system and permanent mold casting, the performance of these components can be enhanced significantly. This will also help to further reduce the weight. However, many technological obstacles need to be overcome before using these alloys in automotive applications in an economical way. There is very limited information in the open literature on gravity and low-pressure permanent mold casting of high strength aluminum alloys. This report summarizes the results and issues encountered during the casting trials of high strength aluminum alloy 206.0 (Al-Cu alloy) and moderate strength alloy 535.0 (Al-Mg alloy). Five engineering components were cast by gravity tilt-pour or low pressure permanent mold casting processes at CanmetMATERIALS (CMAT) and two production foundries. The results of the casting trials show that high integrity engineering components can be produced successfully from both alloys if specific processing parameters are used. It was shown that a combination of melt processing and mold temperature is necessary for the elimination of hot tears in both alloys.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".