Comparative Mechanical Properties of AE42 and AJ52x High-Temperature Diecast Magnesium Alloys for Elevated Temperature Applications
Bibliographic record
Abstract
<div class="htmlview paragraph">For environmental reasons, automotive improved fuel efficiency is becoming increasingly important for OEM's. However, consumers are also demanding more in terms of added comfort and safety features. To meet these conflicting requirements, OEM's are turning to the increased usage of light-weight materials, such as magnesium alloys, for automotive components thus allowing for improved fuel efficiency while maintaining the ability to add extra comfort and safety features. Due to the presence of large castings in the power-train, as well as the significant mass in the front of a vehicle, high temperature magnesium alloys that can meet the service requirements of these components are under investigation. The types of mass reduction applications for these alloys include transmission cases, covers and other structural components.</div> <div class="htmlview paragraph">Diecasting, because of its high productivity, is the preferred manufacturing process for these types of components. For this reason, the investigation of high temperature magnesium alloys for these applications focused on die casting alloys. In particular, the objective was to compare the mechanical properties of die cast AE42 and AJ52x. The same multi-cavity dies were used to manufacture samples of each alloy. One of the dies contained cavities of varying thickness allowing the effect of section thickness on mechanical properties to be investigated, without machining and removing the important die cast surface of the samples. This paper provides a comparative compilation of the tensile properties obtained for the two alloys in the investigation for various temperatures and various thicknesses using as-cast samples. Impact, creep, and fatigue properties were also investigated.</div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".